CN114363178B - Block chain network optimization method, device and system - Google Patents
Block chain network optimization method, device and system Download PDFInfo
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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Abstract
The invention belongs to the technical field of block chains, and particularly relates to a block chain network optimization method, a device and a storage medium, wherein the method comprises the following steps: step 1: establishing a cellular network topological graph of the block chain based on the block chain network structure; in the cellular network topology map, each blockchain node acts as a mesh; step 2: in the running process of the block chain, generating test data, so that the test data is transmitted among all nodes in the block chain, and simultaneously acquiring data parameters of the test data in the transmission process in real time: the data parameters include: compression distortion parameters of the test data and frame impairment distortion parameters of the test data. Based on the structure of the block chain network, the method can realize the traditional network optimization function, analyze the network quality through the cellular network topological graph, and has higher analysis accuracy.
Description
Technical Field
The invention belongs to the technical field of block chains, and particularly relates to an Internet of things terminal access monitoring method, a computer program and a storage medium.
Background
What is the blockchain? From a technological level, the blockchain involves many scientific and technical problems such as mathematics, cryptography, internet and computer programming. From the application perspective, the block chain is simply a distributed shared account book and a database, and has the characteristics of decentralization, no tampering, trace retaining in the whole process, traceability, collective maintenance, openness and transparency and the like. The characteristics ensure the honesty and the transparency of the block chain and lay a foundation for creating trust for the block chain. And the abundant application scene of block chain basically can solve the information asymmetry problem based on the block chain, and realize the cooperative trust and the consistent action among a plurality of main bodies.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism and an encryption algorithm. The block chain (Blockchain) is an important concept of the bitcoin, is essentially a decentralized database, and is used as a bottom technology of the bitcoin, and is a series of data blocks which are generated by correlation through a cryptographic method, wherein each data block contains information of a batch of bitcoin network transactions, and the information is used for verifying the validity (anti-counterfeiting) of the information and generating a next block.
Under the existing network state, users often encounter the problems and troubles that broadband congestion, low application performance, worm virus, DDoS abuse, malicious intrusion and the like have negative influences on network use and resources, the network optimization function is to supplement the existing equipment and network problems such as firewall, security and intrusion detection, load balancing, bandwidth management, network antivirus and the like, parameter acquisition and data analysis can be carried out in a mode of accessing hardware and software operation to find out the reasons influencing the network quality, and the network can achieve the best operation state by technical means or a method for increasing corresponding hardware equipment and adjusting to enable the network resources to obtain the best benefits. Meanwhile, the method can realize the acceleration of network application performance, the management of security content, the management of security events, the management of users, the management and optimization of network resources, the management of desktop systems, the monitoring, measurement, tracking, analysis and management of traffic patterns, and improve the performance of application transmission on the wide area network. The product mainly comprises a network resource manager, an application performance accelerator and a webpage performance accelerator, and network optimization is carried out according to different requirements and functional requirements.
Disclosure of Invention
The invention mainly aims to provide a method, a device and a system for optimizing a block chain network, which are used for constructing a cellular network topological graph of the block chain network based on the structure of the block chain network, and then performing node network quality analysis through the cellular network topological graph, so that final network optimization is performed according to the structure of the node network quality analysis, the traditional network optimization function can be realized, the network quality is analyzed through the cellular network topological graph, the analysis accuracy is higher, and meanwhile, the optimization efficiency is higher.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a method for block chain network optimization, the method performing the steps of:
step 1: establishing a cellular network topological graph of the block chain based on the block chain network structure; in the cellular network topology map, each blockchain node acts as a mesh;
and 2, step: in the running process of the block chain, generating test data, so that the test data is transmitted among all nodes in the block chain, and simultaneously acquiring data parameters of the test data in the transmission process in real time: the data parameters include: compression distortion parameters of the test data and frame damage distortion parameters of the test data;
and step 3: calculating to obtain a test data quality parameter based on a data parameter of the obtained test data in the transmission process; converting the test data quality parameters into the network quality values of each node according to a preset function conversion rule;
and 4, step 4: marking the network quality value of each node in each corresponding grid in the cellular network topological graph; according to the marked network quality value, sorting, according to the sorting result, marking a serial number for each grid, wherein the serial number of the grid with the highest network quality value is 1;
and 5: according to the sequence number of each grid, adjusting the position of each grid in the cellular network topological graph, and placing the grid with the grid sequence number of 1 in the center of the cellular network topological graph, so that the sequence number of the grid from inside to outside in the cellular network topological graph is from low to high, and a new cellular network topological graph is generated;
step 6: comparing the new cellular network topological graph with the position of each same grid in the cellular network topological graph, and recording if the position of the same grid externally arranged in the new cellular network topological graph and the position of the same grid in the cellular network topological graph are changed;
and 7: comparing the number of the grids with the position change with a set threshold value, judging whether network optimization is carried out or not according to the comparison result, if the network optimization is carried out, calling a preset optimization model, and judging whether a cooperative node needs to be added to each node or not based on the network quality value of each node so as to improve the network quality value of the node and complete the network optimization; the cooperative work node is a standby node preset in the block chain, the cooperative work node is connected with the node in parallel, and when the cooperative work node and the node work at the same time, the performance of the node is improved.
Further, in the step 3: the method for calculating the data quality of the test data in the transmission process comprises the following steps: acquiring compression distortion parameters of test data; obtaining frame damage distortion parameters; calculating a test data quality parameter according to the compression distortion parameter and the frame damage distortion parameter, wherein the test data quality parameter is a difference value of the compression distortion parameter and the frame damage distortion parameter; wherein the obtaining the frame impairment distortion parameters comprises: obtaining the proportion of damaged frames, the average damage degree of the damaged frames and the damage frequency from the packet headers of the test data packets; obtaining compression distortion parameters of test data; and calculating a frame damage distortion parameter of the test data by using the compression distortion parameter, the proportion of damaged frames, the average damage degree of the damaged frames and the damage frequency, wherein the frame damage distortion parameter is between 0 and the difference between the compression distortion parameter and the minimum quality value, and the quality parameter of the test data is reduced to the minimum quality value along with the increase of any one of the proportion of damaged frames, the average damage degree of the damaged frames and the damage frequency.
Further, the obtaining of the compression distortion parameter of the test data includes: obtaining the code rate and the frame rate of the test data stream; calculating a compression distortion parameter of the test data according to the code rate and the frame rate of the test data stream, wherein the compression distortion parameter is increased along with the increase of the code rate until the compression distortion parameter is the maximum value; at a certain code rate, the compression distortion parameter decreases with the increase of the frame rate.
Further, the obtaining of the compression distortion parameter of the test data includes: obtaining the code rate and the frame rate of the test data stream and the complexity of the content of the test data; calculating a compression distortion parameter of the test data according to the code rate and the frame rate of the test data stream and the complexity of the content of the test data, wherein the compression distortion parameter is increased along with the increase of the code rate until the compression distortion parameter is the maximum value and is reduced along with the increase of the complexity of the content of the test data; at a certain code rate, the compression distortion parameter decreases with the increase of the frame rate.
Further, the method for converting the test data quality parameter into the network quality value of each node according to the preset function conversion rule in the step 3 includes: node network quality value = test data measurement parameter/10.
Further, the method for comparing the number of grids with which the position change is recorded with the set threshold value in step 7 and determining whether to perform network optimization according to the comparison result includes: setting two thresholds N and M, wherein M > N; if the number of the grids with the position change is recorded to be less than N, judging that network optimization is not needed; if the number of the grids with the position change is larger than M, judging that network optimization is needed; and if N < the number of grids with position change recorded < M, judging that semi-optimization is needed, and generating a semi-optimization parameter S.
Further, the number of grids when the position change occurs is recorded>And M, when the network optimization is judged to be needed, the optimization model preset in the step 7 is expressed by using the following formula:
y is a generated judgment value, and whether the node is subjected to network optimization is judged according to the comparison between the judgment value and a set judgment threshold value; wherein n is the number of grids with position change, beta is an adjustment coefficient, and the value range is 1-3; x is the network quality value of each node.
Further, when N is<Recording the number of grids where the position change occurs<M, when it is determined that semi-optimization is required, and a semi-optimization parameter S is generated, the optimization model preset in step 7 is represented by the following formula:
y is a generated judgment value, and whether the node is subjected to network optimization is judged according to the comparison between the judgment value and a set judgment threshold value; wherein n is the number of grids with position change, beta is an adjustment coefficient, and the value range is 1-3; x is the network quality value of each node.
Block chain network optimization device.
A storage medium storing a method for optimizing a blockchain network.
The block chain network optimization method, device and system of the invention haveThe following beneficial effects: the cellular network topological graph of the blockchain network is constructed based on the structure of the blockchain network, and then the node network quality analysis is carried out through the cellular network topological graph, so that the final network optimization is carried out according to the structure of the node network quality analysis, the traditional network optimization function can be realized, the network quality is analyzed through the cellular network topological graph, the analysis accuracy is higher, and meanwhile, the optimization efficiency is higher. The method is mainly realized by the following steps: 1. network analysis is performed by constructing a cellular network topology map: the invention constructs the cellular network topological graph of the block chain; according to the sequence number of each grid, the position of each grid in the cellular network topological graph is adjusted, the grid with the grid sequence number of 1 is arranged in the center of the cellular network topological graph, so that the sequence number of the grid from inside to outside in the cellular network topological graph is from low to high, a new cellular network topological graph is generated, the positions of each same grid in the new cellular network topological graph and the cellular network topological graph are compared, if the position of the same grid externally arranged in the new cellular network topological graph and the position of the same grid in the cellular network topological graph are changed, network analysis is realized, the quality of a block chain network is integrally grasped, then network optimization is carried out, and the optimization efficiency can be obviously improved; 2. calculating the network quality of the nodes: in the running process of the block chain, the invention generates a piece of test data, so that the test data is transmitted among all nodes in the block chain, and simultaneously, the data parameters of the test data in the transmission process are acquired in real time: the data parameters include: the method comprises the steps that compression distortion parameters of test data and frame damage distortion parameters of the test data are calculated to obtain quality parameters of the test data based on data parameters of the obtained test data in a transmission process; the test data quality parameters are converted into the network quality value of each node according to a preset function conversion rule, and through the process, the network quality of each node in the block chain network can be clearly known, so that when the block chain network is optimized, different modes can be optimized according to different node conditions, and the optimization effect and efficiency are improved; 3. judging whether the node is optimized or not: when the invention judges whether the node needs to be optimized, the used algorithm is as the following formula tableThe following steps:
y is a generated judgment value, and whether the node is subjected to network optimization is judged according to the comparison between the judgment value and a set judgment threshold value; wherein n is the number of grids with position change, beta is an adjustment coefficient, and the value range is 1-3; x is the network quality value of each node; by the algorithm, whether the current node needs to be optimized or not can be judged more scientifically, the accuracy of optimization is improved, and the efficiency of optimization is improved indirectly.
Drawings
Fig. 1 is a flowchart illustrating a method for optimizing a blockchain network according to an embodiment of the present invention;
fig. 2 is a cellular network topology diagram of a method, an apparatus and a storage medium for optimizing a blockchain network according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an experimental effect of the block chain network optimization method, device and storage medium according to the embodiment of the present invention, in which the network efficiency gain multiple changes with the number of experiments, compared with the prior art.
Detailed Description
The technical solution of the present invention is further described in detail below with reference to the following detailed description and the accompanying drawings:
example 1
As shown in fig. 1, a block chain network optimization method performs the following steps:
step 1: establishing a cellular network topological graph of the block chain based on the block chain network structure; in the cellular network topology map, each blockchain node acts as a mesh;
step 2: in the running process of the block chain, generating test data, so that the test data is transmitted among all nodes in the block chain, and simultaneously acquiring data parameters of the test data in the transmission process in real time: the data parameters include: compression distortion parameters of the test data and frame damage distortion parameters of the test data;
and step 3: calculating to obtain a test data quality parameter based on the data parameter of the obtained test data in the transmission process; converting the test data quality parameters into the network quality value of each node according to a preset function conversion rule;
and 4, step 4: marking the network quality value of each node in each corresponding grid in the cellular network topological graph; sequencing according to the marked network quality values, marking a sequence number for each grid according to a sequencing result, wherein the sequence number of the grid with the highest network quality value is 1;
and 5: according to the sequence number of each grid, adjusting the position of each grid in the cellular network topological graph, and placing the grid with the grid sequence number of 1 in the center of the cellular network topological graph, so that the sequence number of the grid from inside to outside in the cellular network topological graph is from low to high, and a new cellular network topological graph is generated;
and 6: comparing the new cellular network topological graph with the position of each same grid in the cellular network topological graph, and recording if the position of the same grid externally arranged in the new cellular network topological graph and the position of the same grid in the cellular network topological graph are changed;
and 7: comparing the number of the grids with the recorded position change with a set threshold, judging whether to perform network optimization according to a comparison result, if so, calling a preset optimization model, and judging whether each node needs to be added with a cooperative work node based on the network quality value of each node so as to improve the network quality value of the node and complete the network optimization; the cooperative work node is a standby node preset by the block chain, the cooperative work node is connected with the node in parallel, and when the cooperative work node and the node work simultaneously, the performance of the node is improved.
Specifically, the cellular network topology map of the blockchain network is constructed based on the structure of the blockchain network, and then the cellular network topology map is used for carrying out node network quality analysis, so that the final network optimization is carried out according to the structure of the node network quality analysis, and the traditional network optimization can be realizedThe network quality is analyzed through the cellular network topological graph, the analysis accuracy is higher, and meanwhile, the optimization efficiency is higher. The method is mainly realized by the following steps: 1. network analysis is performed by constructing a cellular network topology map: the invention constructs the cellular network topological graph of the block chain; according to the sequence number of each grid, the position of each grid in the cellular network topological graph is adjusted, the grid with the grid sequence number of 1 is arranged in the center of the cellular network topological graph, so that the sequence number of the grid from inside to outside in the cellular network topological graph is from low to high, a new cellular network topological graph is generated, the positions of each same grid in the new cellular network topological graph and the cellular network topological graph are compared, if the position of the same grid externally arranged in the new cellular network topological graph and the position of the same grid in the cellular network topological graph are changed, network analysis is realized, the quality of a block chain network is integrally grasped, then network optimization is carried out, and the optimization efficiency can be obviously improved; 2. calculating the network quality of the nodes: in the running process of the block chain, the invention generates a piece of test data, so that the test data is transmitted among all nodes in the block chain, and simultaneously, the data parameters of the test data in the transmission process are acquired in real time: the data parameters include: calculating a compression distortion parameter of the test data and a frame damage distortion parameter of the test data based on a data parameter of the obtained test data in a transmission process to obtain a quality parameter of the test data; the test data quality parameters are converted into the network quality value of each node according to a preset function conversion rule, and through the process, the network quality of each node in the block chain network can be clearly known, so that when the block chain network is optimized, different modes can be optimized according to different node conditions, and the optimization effect and efficiency are improved; 3. judging whether the node is optimized or not: when judging whether the node needs to be optimized, the algorithm used by the invention is expressed by the following formula:
y is a generated judgment value, and whether the node is subjected to network optimization is judged according to the comparison between the judgment value and a set judgment threshold value; wherein n is hairGenerating the number of grids with changed positions, wherein beta is an adjustment coefficient and the value range is 1-3; x is the network quality value of each node; by the algorithm, whether the current node needs to be optimized or not can be judged more scientifically, the accuracy of optimization is improved, and the efficiency of optimization is improved indirectly.
Example 2
On the basis of the above embodiment, in step 3: the method for calculating the data quality of the test data in the transmission process comprises the following steps: acquiring compression distortion parameters of test data; obtaining frame damage distortion parameters; calculating a test data quality parameter according to the compression distortion parameter and the frame damage distortion parameter, wherein the test data quality parameter is a difference value of the compression distortion parameter and the frame damage distortion parameter; wherein the obtaining the frame impairment distortion parameters comprises: obtaining the proportion of damaged frames, the average damage degree of the damaged frames and the damage frequency from the packet headers of the test data packets; obtaining compression distortion parameters of test data; and calculating a frame damage distortion parameter of the test data by using the compression distortion parameter, the proportion of damaged frames, the average damage degree of the damaged frames and the damage frequency, wherein the frame damage distortion parameter is between 0 and the difference between the compression distortion parameter and the minimum quality value, and the quality parameter of the test data is reduced to the minimum quality value along with the increase of any one of the proportion of damaged frames, the average damage degree of the damaged frames and the damage frequency.
Specifically, the data distortion refers to a phenomenon that the original real data is changed by a computer or a human factor, so that a data result is deviated from the real data. The most common data distortion in life is statistical data distortion, and the loss and harm caused by data distortion are huge, and the data distortion must be strictly prevented.
Distortion, also known as "distortion," refers to the deviation of a signal from its original signal or standard during transmission. The data distortion refers to the phenomenon that the original real data is changed by a computer or a human factor, so that the data result is deviated from the real data. The most common data distortion in life is statistical data distortion, and the loss and harm caused by data distortion are huge, and the data distortion must be strictly prevented.
Example 3
On the basis of the above embodiment, the obtaining of the compression distortion parameter of the test data includes: obtaining the code rate and the frame rate of the test data stream; calculating a compression distortion parameter of the test data according to the code rate and the frame rate of the test data stream, wherein the compression distortion parameter is increased along with the increase of the code rate until the compression distortion parameter is the maximum value; at a certain code rate, the compression distortion parameter decreases with the increase of the frame rate.
Specifically, the code rate is also called bit rate, and refers to the number of bits (bits) transmitted per second. The unit is bps (bit per second), which is also expressed as b/s, and the higher the bit rate, the larger the amount of data (number of bits) transferred per unit time. Information in a computer is represented by binary 0 and 1, where each 0 or 1 is called a bit, and is represented by a lower case b, i.e., bit. Upper case B represents byte, i.e., byte, 1 byte =8 bits, i.e., 1b =8B. The size unit of a file is represented, and a Kilobyte (KB) is generally used to represent the size of the file.
Most bit rate control schemes include two parts. A portion of the encoded bitstream output by the encoder is input into a buffer. For a constant bit rate channel, the data in the buffer is fetched at a constant rate, and if the buffer is large enough, bit rate variations due to MPEG picture type etc. can be smoothed out. This is required for both constant bit rate transmission and generally variable bit rate transmission. However, in practice the size of the buffer is always limited. The buffering process causes a delay to the system, which is proportional to the size of the buffer. Latency is generally a serious problem for real-time image communication, so the buffer should be kept as small as possible. That is, a bit rate long-term fluctuation due to a change in scene content or a switch or the like cannot be smoothed out in this way and another part is required. This is to feed some measure of the output bit rate back to the encoder to control the encoding process and thereby change the output bit rate.
Example 4
On the basis of the above embodiment, the obtaining compression distortion parameters of the test data includes: obtaining the code rate and the frame rate of the test data stream and the complexity of the content of the test data; calculating a compression distortion parameter of the test data according to the code rate and the frame rate of the test data stream and the complexity of the content of the test data, wherein the compression distortion parameter is increased along with the increase of the code rate until the compression distortion parameter is the maximum value and is reduced along with the increase of the complexity of the content of the test data; at a certain code rate, the compression distortion parameter decreases with the increase of the frame rate.
Example 5
On the basis of the previous embodiment, the method for converting the test data quality parameter into the network quality value of each node according to the preset function conversion rule in the step 3 includes: node network quality value = test data measurement parameter/10.
Example 6
On the basis of the previous embodiment, the method for comparing the number of grids with the recorded position change with the set threshold in step 7, and determining whether to perform network optimization according to the comparison result includes: setting two thresholds N and M, wherein M > N; if the number of the grids with the position change is recorded to be less than N, judging that network optimization is not needed; if the number of the grids with the position change is larger than M, judging that network optimization is needed; and if N < the number of grids recording position change < M, judging that semi-optimization is required, and generating a semi-optimization parameter S.
Specifically, under the existing network state, users often encounter problems and troubles that broadband congestion, low application performance, worm virus, DDoS abuse, malicious intrusion and the like have negative influences on network use and resources, the network optimization function is to supplement the existing devices and network problems such as firewall, security and intrusion detection, load balancing, bandwidth management, network antivirus and the like, parameter acquisition and data analysis can be carried out in a mode of accessing hardware and software operation, the reason influencing the network quality is found out, and the network can achieve the best running state through technical means or a method for increasing corresponding hardware devices and adjusting, so that the network resources obtain the best benefits. Meanwhile, the network application performance acceleration, the security content management, the security event management, the user management, the network resource management and optimization, the desktop system management, the traffic pattern monitoring, measurement, tracking, analysis and management are realized, and the function of applying the transmission performance on the wide area network is improved. The product mainly comprises a network resource manager, an application performance accelerator and a webpage performance accelerator, and network optimization is carried out according to different requirements and functional requirements.
Example 7
On the basis of the above embodiment, the number of grids when the position change occurs is recorded>And M, when judging that network optimization is needed, the optimization model preset in the step 7 is expressed by using the following formula:
y is a generated judgment value, and whether the node is subjected to network optimization is judged according to the comparison between the judgment value and a set judgment threshold value; wherein n is the number of grids with position change, beta is an adjustment coefficient, and the value range is 1-3; x is the network quality value of each node.
Example 8
On the basis of the above embodiment, when N is<Recording the number of grids where the position change occurs<M, when it is determined that semi-optimization is required, and a semi-optimization parameter S is generated, the optimization model preset in step 7 is represented by the following formula:
y is a generated judgment value, and whether the node is subjected to network optimization is judged according to the comparison between the judgment value and a set judgment threshold value; wherein n is the number of grids with position change, beta is an adjustment coefficient, and the value range is 1-3; x is the network quality value of each node.
Example 9
Block chain network optimization device.
Example 10
A storage medium storing a method for optimizing a blockchain network.
The above description is only an embodiment of the present invention, but not intended to limit the scope of the present invention, and any structural changes made according to the present invention should be considered as being limited within the scope of the present invention without departing from the spirit of the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art will appreciate that the various illustrative modules, method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (8)
1. Method for optimizing a blockchain network, characterized in that the method performs the following steps:
step 1: establishing a cellular network topological graph of the block chain based on the block chain network structure; in the cellular network topology map, each blockchain node acts as a mesh;
step 2: in the running process of the block chain, generating test data, so that the test data is transmitted among all nodes in the block chain, and simultaneously acquiring data parameters of the test data in the transmission process in real time: the data parameters include: compression distortion parameters of the test data and frame damage distortion parameters of the test data;
and step 3: calculating to obtain a test data quality parameter based on a data parameter of the obtained test data in the transmission process; converting the test data quality parameters into the network quality values of each node according to a preset function conversion rule;
and 4, step 4: marking the network quality value of each node in each corresponding grid in the cellular network topological graph; according to the marked network quality value, sorting, according to the sorting result, marking a serial number for each grid, wherein the serial number of the grid with the highest network quality value is 1;
and 5: according to the sequence number of each grid, adjusting the position of each grid in the cellular network topological graph, and placing the grid with the grid sequence number of 1 in the center of the cellular network topological graph, so that the sequence number of the grid from inside to outside in the cellular network topological graph is from low to high, and a new cellular network topological graph is generated;
step 6: comparing the new cellular network topological graph with the position of each same grid in the cellular network topological graph, and recording if the position of the same grid externally arranged in the new cellular network topological graph and the position of the same grid in the cellular network topological graph are changed;
and 7: comparing the number of the grids with the recorded position change with a set threshold, judging whether to perform network optimization according to a comparison result, if so, calling a preset optimization model, and judging whether each node needs to be added with a cooperative work node based on the network quality value of each node so as to improve the network quality value of the node and complete the network optimization; the cooperative working node is a standby node preset by a block chain, the cooperative working node and the node are connected in parallel, and when the cooperative working node and the node work simultaneously, the performance of the node is improved;
in the step 3: the method for calculating the data quality of the test data in the transmission process comprises the following steps: acquiring compression distortion parameters of test data; obtaining frame damage distortion parameters; calculating a test data quality parameter according to the compression distortion parameter and the frame damage distortion parameter, wherein the test data quality parameter is a difference value of the compression distortion parameter and the frame damage distortion parameter; wherein the obtaining the frame impairment distortion parameters comprises: obtaining the proportion of damaged frames, the average damage degree of the damaged frames and the damage frequency from the packet headers of the test data packets; obtaining compression distortion parameters of test data; and calculating a frame damage distortion parameter of the test data by using the compression distortion parameter, the proportion of damaged frames, the average damage degree of the damaged frames and the damage frequency, wherein the frame damage distortion parameter is between 0 and the difference between the compression distortion parameter and the minimum quality value, and the quality parameter of the test data is reduced to the minimum quality value along with the increase of any one of the proportion of damaged frames, the average damage degree of the damaged frames and the damage frequency.
2. The method of claim 1, wherein obtaining the compression distortion parameters for the test data comprises: obtaining the code rate and the frame rate of the test data stream; calculating a compression distortion parameter of the test data according to the code rate and the frame rate of the test data stream, wherein the compression distortion parameter is increased along with the increase of the code rate until the compression distortion parameter is the maximum value; at a certain code rate, the compression distortion parameter decreases with the increase of the frame rate.
3. The method of claim 2, wherein obtaining the compression distortion parameters for the test data comprises: obtaining the code rate and the frame rate of the test data stream and the complexity of the content of the test data; calculating a compression distortion parameter of the test data according to the code rate and the frame rate of the test data stream and the complexity of the content of the test data, wherein the compression distortion parameter is increased along with the increase of the code rate until the compression distortion parameter is the maximum value and is reduced along with the increase of the complexity of the content of the test data; at a certain code rate, the compression distortion parameter decreases with the increase of the frame rate.
4. The method of claim 3, wherein the step 3 of converting the test data quality parameter into the network quality value of each node according to a predetermined functional conversion rule comprises: node network quality value = test data measurement parameter/10.
5. The method as claimed in claim 4, wherein the step 7 of comparing the number of grids recorded with the position change with a set threshold value, and determining whether to perform network optimization according to the comparison result comprises: setting two thresholds N and M, wherein M > N; if the number of the grids with the position change is recorded to be less than N, judging that network optimization is not needed; if the number of the grids with the position change is larger than M, judging that network optimization is needed; and if N < the number of grids with position change recorded < M, judging that semi-optimization is needed, and generating a semi-optimization parameter S.
6. The method of claim 5, wherein the number of grids that change when a change in position occurs is recorded>And M, when the network optimization is judged to be needed, the optimization model preset in the step 7 is expressed by using the following formula:
y is a generated judgment value, and whether the node is subjected to network optimization is judged according to the comparison between the judgment value and a set judgment threshold value; wherein n is the number of grids with position change, beta is an adjustment coefficient, and the value range is 1-3; x is the network quality value of each node.
7. The method of claim 5, wherein when N is<Recording the number of grids where the position change occurs<M, when it is determined that semi-optimization is required, and a semi-optimization parameter S is generated, the optimization model preset in step 7 is represented by the following formula:
wherein Y is the generated judgment value, and the ratio is carried out according to the judgment value and the set judgment threshold valueJudging whether to perform network optimization on the node or not; wherein n is the number of grids with position change, beta is an adjustment coefficient, and the value range is 1-3; x is the network quality value of each node.
8. Storage medium for implementing the method according to one of claims 1 to 7, wherein a computer program is stored on the storage medium, which computer program, when being executed by a processor, performs the method for block chain network optimization according to one of claims 1 to 7.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2434702A1 (en) * | 2010-09-28 | 2012-03-28 | Alcatel Lucent | Scheduling method for data streaming in a telecommunication network infrastructure |
| CN110474818A (en) * | 2019-07-11 | 2019-11-19 | 福州博泉网络科技有限公司 | Block chain network sniffer, Network Sniffing method and optimization method |
| CN110601874A (en) * | 2019-08-14 | 2019-12-20 | 深圳壹账通智能科技有限公司 | Block chain deployment method, device, computer equipment and storage medium |
| CN111343003A (en) * | 2020-02-11 | 2020-06-26 | 广州智乐物联网技术有限公司 | Data analysis method and device based on block chain and SDN edge computing network system |
| CN111585823A (en) * | 2020-05-20 | 2020-08-25 | 石高峰 | Communication network optimization method and device based on block chain and edge calculation |
| CN112104558A (en) * | 2020-10-30 | 2020-12-18 | 上海交通大学 | Method, system, terminal and medium for implementing block chain distribution network |
-
2022
- 2022-01-21 CN CN202210072183.2A patent/CN114363178B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2434702A1 (en) * | 2010-09-28 | 2012-03-28 | Alcatel Lucent | Scheduling method for data streaming in a telecommunication network infrastructure |
| CN110474818A (en) * | 2019-07-11 | 2019-11-19 | 福州博泉网络科技有限公司 | Block chain network sniffer, Network Sniffing method and optimization method |
| CN110601874A (en) * | 2019-08-14 | 2019-12-20 | 深圳壹账通智能科技有限公司 | Block chain deployment method, device, computer equipment and storage medium |
| CN111343003A (en) * | 2020-02-11 | 2020-06-26 | 广州智乐物联网技术有限公司 | Data analysis method and device based on block chain and SDN edge computing network system |
| CN111585823A (en) * | 2020-05-20 | 2020-08-25 | 石高峰 | Communication network optimization method and device based on block chain and edge calculation |
| CN112104558A (en) * | 2020-10-30 | 2020-12-18 | 上海交通大学 | Method, system, terminal and medium for implementing block chain distribution network |
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