CN111404825A - Data transmission method, system, computer device and storage medium - Google Patents
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Abstract
The scheme relates to a data transmission method. The method comprises the following steps: acquiring a fuzzy fractional order ordinary differential equation, and extracting a matrix index of the fuzzy fractional order ordinary differential equation; optimizing the matrix index by using a fine integration method to obtain a target matrix index, and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index; obtaining a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion item; acquiring data to be transmitted, and acquiring the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted; and transmitting the data to be transmitted through the SDN network architecture according to the total calculated amount. Because the matrix index calculation is optimized by using the fuzzy fractional order ordinary differential equation fine integral method, an optimization formula is obtained according to the matrix weighting index and the matrix expansion term, and the bandwidth can be optimized by combining the SDN network framework for data transmission, thereby improving the efficiency of data transmission.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data transmission method, a data transmission system, a computer device, and a storage medium.
Background
With the rapid development of computer internet technology and the popularization of high-performance computers, people have increasingly enriched network life, the data form of the internet for exchanging information under the background of cloud computing and big data technology is also greatly changed, and the requirement on data transmission is higher and higher. Data transmission refers to the process of transmitting data between a data source and a data sink via one or more links according to an appropriate protocol, and also refers to the operation of transmitting data from one location to another by means of signals on a channel. In data transmission, data transmission rate is one of the important technical indicators describing a data transmission system, data is often transmitted through a channel, and bandwidth can be used to indicate the capability of the channel to transmit data.
However, the conventional data transmission method has a problem of low transmission efficiency.
Disclosure of Invention
Based on this, in order to solve the above technical problem, a data transmission method, a system, a computer device and a storage medium are provided, which can improve the efficiency of data transmission.
A method of data transmission, the method comprising:
acquiring a fuzzy fractional order ordinary differential equation, and extracting a matrix index of the fuzzy fractional order ordinary differential equation;
optimizing the matrix index by using a fine integration method to obtain a target matrix index, and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index;
obtaining a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion item;
acquiring data to be transmitted, and acquiring the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted;
and transmitting the data to be transmitted through an SDN network architecture according to the total calculated amount.
In one embodiment, the optimizing the matrix index by using a fine integration method to obtain a target matrix index includes:
calculating an inverse matrix of a matrix corresponding to the matrix index;
carrying out pad series approximation on the matrix index by an incremental storage technology to obtain a matrix weighting index;
and obtaining an approximate value of the matrix index according to the inverse matrix and the matrix weighting index by using the fine integration method, and taking the approximate value of the matrix index as the target matrix index.
In one embodiment, the obtaining an adaptive selection optimization formula according to the matrix weighting index and the matrix expansion term includes:
obtaining the relation between the matrix weighting index and the matrix expansion item;
calculating the relative error between the matrix weighting index and the matrix expansion item according to the relation;
and obtaining the self-adaptive selection optimization formula according to the relative error.
In one embodiment, the obtaining the adaptive selection optimization formula according to the relative error includes:
obtaining a first change formula of the matrix weighting index according to the relative error, and obtaining a second change formula of the matrix expansion item according to the relative error;
obtaining the self-adaptive selection optimization formula according to the first variation formula and the second variation formula;
the adaptive selection optimization formula comprises an adaptive selection optimization formula of the matrix weighting index and an adaptive selection optimization formula of the matrix expansion term.
In one embodiment, the data transmission of the data to be transmitted through an SDN network architecture according to the total calculated amount includes:
dividing the data to be transmitted according to the total calculated amount to obtain each transmission subdata;
distributing task nodes for the transmission subdata through an API (application programming interface) of the SDN network architecture;
and carrying out data transmission through the task node.
In one embodiment, the performing, by the task node, data transmission includes:
processing data in the task node through an open flow technology to obtain processed data;
acquiring an openflow controller corresponding to the openflow technology;
and carrying out data transmission on the processed data through the open flow controller.
A data transmission system, the system comprising:
the matrix index extraction module is used for acquiring a fuzzy fractional order ordinary differential equation and extracting a matrix index of the fuzzy fractional order ordinary differential equation;
the target matrix index acquisition module is used for optimizing the matrix index by using a fine integration method to obtain a target matrix index and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index;
the formula acquisition module is used for acquiring a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion item;
the calculation module is used for acquiring data to be transmitted and obtaining the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted;
and the data transmission module is used for transmitting the data to be transmitted through an SDN network architecture according to the total calculated amount.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a fuzzy fractional order ordinary differential equation, and extracting a matrix index of the fuzzy fractional order ordinary differential equation;
optimizing the matrix index by using a fine integration method to obtain a target matrix index, and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index;
obtaining a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion item;
acquiring data to be transmitted, and acquiring the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted;
and transmitting the data to be transmitted through an SDN network architecture according to the total calculated amount.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring a fuzzy fractional order ordinary differential equation, and extracting a matrix index of the fuzzy fractional order ordinary differential equation;
optimizing the matrix index by using a fine integration method to obtain a target matrix index, and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index;
obtaining a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion item;
acquiring data to be transmitted, and acquiring the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted;
and transmitting the data to be transmitted through an SDN network architecture according to the total calculated amount.
According to the data transmission method, the data transmission system, the computer equipment and the storage medium, the matrix index of the fuzzy fractional order ordinary differential equation is extracted by acquiring the fuzzy fractional order ordinary differential equation; optimizing the matrix index by using a fine integration method to obtain a target matrix index, and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index; obtaining a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion item; acquiring data to be transmitted, and acquiring the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted; and transmitting the data to be transmitted through the SDN network architecture according to the total calculated amount. Because the matrix index calculation is optimized by using the fuzzy fractional order ordinary differential equation fine integral method, an optimization formula is obtained according to the matrix weighting index and the matrix expansion term, and the bandwidth can be optimized by combining the SDN network framework for data transmission, thereby improving the efficiency of data transmission.
Drawings
FIG. 1 is a diagram of an exemplary data transmission method;
FIG. 2 is a flow diagram illustrating a method for data transmission according to one embodiment;
FIG. 3 is a flow diagram of adaptive selection of parameters in one embodiment;
figure 4 is a schematic diagram of an SDN network architecture in one embodiment;
FIG. 5 is a diagram illustrating the structure of an open flow network in one embodiment;
FIG. 6 is a diagram illustrating task execution by an information application system in one embodiment;
FIG. 7 is a diagram illustrating task scheduling by a control module in one embodiment;
FIG. 8 is a diagram illustrating scheduling of data transmission module operations in one embodiment;
FIG. 9 is a graphical illustration of numerical accuracy and error comparison of the algorithm before and after improvement in the experiment;
FIG. 10 is a graph showing a comparison of computational efficiency before and after algorithm improvement in an experiment;
FIG. 11 is a graph illustrating the comparison of port data size and data merging rate of the system before and after improvement in the experiment;
FIG. 12 is a diagram illustrating a comparison of system task completion times before and after improvement under different bandwidths in an experiment;
FIG. 13 is a block diagram of the structure of a data transmission system in one embodiment;
FIG. 14 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It will be understood that the terms "first," "second," and the like as used herein may be used herein to describe various conditions, but these conditions are not limited by these terms. These terms are only used to distinguish a first condition from another condition. For example, a first variation formula may be referred to as a second variation formula, and similarly, a second variation formula may be referred to as a first variation formula, without departing from the scope of the present application. Both the first variation formula and the second variation formula are variation formulas, but they are not the same variation formula.
The data transmission method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. As shown in FIG. 1, the application environment includes a computer device 110. The computer device 110 may obtain the fuzzy fractional order ordinary differential equation, and extract a matrix index of the fuzzy fractional order ordinary differential equation; the computer device 110 may optimize the matrix index by using a fine integration method to obtain a target matrix index, and obtain a matrix weighting index and a matrix expansion term corresponding to the target matrix index; the computer device 110 may obtain the adaptive selection optimization formula according to the matrix weighting index and the matrix expansion term; the computer device 110 may also obtain data to be transmitted, and obtain a total calculation amount of the adaptive selection optimization formula according to the data to be transmitted; and transmitting the data to be transmitted through the SDN network architecture according to the total calculated amount. The computer device 110 may be, but is not limited to, various personal computers, laptops, smartphones, robots, unmanned aerial vehicles, tablets, portable wearable devices, and the like.
In one embodiment, as shown in fig. 2, there is provided a data transmission method, including the steps of:
step 202, acquiring a fuzzy fractional order ordinary differential equation, and extracting a matrix index of the fuzzy fractional order ordinary differential equation.
The fractional order ordinary differential equation is a pure theoretical method and mainly comprises a calculation method of any order derivative and fine integration. The fuzzy fractional order ordinary differential equation is a calculus equation that combines fuzzy characteristics with fractional order calculus. The matrix index is a matrix function of the square matrix, similar to the exponential function.
The computer equipment can obtain the fuzzy fractional order ordinary differential equation, so that a corresponding equation matrix is obtained according to the fuzzy fractional order ordinary differential equation, and further, a matrix index of the fuzzy fractional order ordinary differential equation is extracted.
And 204, optimizing the matrix index by using a fine integration method to obtain a target matrix index, and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index.
The fine integration method is a semi-analytic numerical method for solving parabolic partial differential equations. The fine integration method can utilize the basic principle of multi-scale wavelet transformation to construct a multi-scale wavelet interpolation operator, then utilize the operator to disperse partial differential equations into ordinary differential equation sets, and finally utilize the fine integration method to solve the equation sets.
After the matrix index is extracted and obtained by the computer equipment, the obtained matrix index can be optimized by using a fine integration method to obtain a target matrix index. Specifically, the computer device may perform weighting or the like on the obtained matrix index by using a fine integration method, thereby obtaining a matrix weighting index and a matrix expansion term corresponding to the target matrix index.
And step 206, obtaining a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion term.
The self-adaptive selection optimization formula is used for reducing the influence of various interference factors on the matrix weighting index and the matrix expansion item during data transmission. The computer equipment can obtain a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion term.
And step 208, acquiring data to be transmitted, and obtaining the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted.
The data to be transmitted may be used to represent data that needs to be transmitted from a data source to a data sink, and the data to be transmitted may be a combination of letters, numbers, symbols, and the like. The total amount of computation may be used to represent the number of computations that need to be performed. After the computer device obtains the data to be transmitted, the total calculation amount of the self-adaptive selection optimization formula can be further obtained according to the data to be transmitted.
And step 210, transmitting the data to be transmitted through the SDN network architecture according to the total calculated amount.
The SDN network architecture is a network control framework, which can manage the operation of the entire network, has strong flexibility, and improves the efficiency of big data calculation. The operation functions of the SDN network architecture are mainly divided into a network management function and a network forwarding function, the operation of the control module and the operation of the data module are separated, the operation efficiency of the whole system network can be improved, the SDN network architecture provides a very flexible API (application programming interface) for upper-layer application, the automatic control capability of the network is improved, and new network application can be operated more easily.
The computer device can divide the data to be transmitted according to the obtained total calculated amount, so that the divided data to be transmitted are subjected to data transmission through the SDN network architecture.
In the embodiment, the matrix index of the fuzzy fractional order ordinary differential equation is extracted by obtaining the fuzzy fractional order ordinary differential equation; optimizing the matrix index by using a fine integration method to obtain a target matrix index, and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index; obtaining a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion item; acquiring data to be transmitted, and acquiring the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted; and transmitting the data to be transmitted through the SDN network architecture according to the total calculated amount. Because the matrix index calculation is optimized by using the fuzzy fractional order ordinary differential equation fine integral method, an optimization formula is obtained according to the matrix weighting index and the matrix expansion term, and the bandwidth can be optimized by combining the SDN network framework for data transmission, thereby improving the efficiency of data transmission.
In an embodiment, the provided data transmission method may further include a process of obtaining a target matrix index, where the specific process includes: calculating an inverse matrix of a matrix corresponding to the matrix index; carrying out pad series approximation on the matrix index by an incremental storage technology to obtain a matrix weighting index; and obtaining an approximate value of the matrix index by using a fine integration method according to the inverse matrix and the matrix weighting index, and taking the approximate value of the matrix index as the target matrix index.
The inverse matrix is a square matrix, for example, if a is an n-order matrix in the number domain, and if another n-order matrix B exists in the same number domain such that AB ═ BA ═ E, then B is the inverse of a. Incremental storage techniques may be used to indicate that storage is constantly taking place and that most of the previously stored content can be saved. In the pad series approximation, pad approximation is a special type of rational approximation for function values.
After the computer device obtains the matrix index, the matrix corresponding to the matrix index can be obtained, and then the inverse matrix of the matrix is calculated. The calculated inverse matrix has obvious diagonal matrix advantage, has good numerical characteristic and can not cause the problem of data stability. The computer equipment can perform pad series approximation on the matrix index by using the property of the matrix index through an incremental storage technology, so as to obtain the matrix weighting index. The computer device may use a fine integration method to perform multiple calculations based on the inverse matrix and the matrix weighting index using a superposition principle and incremental storage to obtain an approximation of the matrix index. The computer device may use an approximation of the matrix index as the target matrix index.
In the present embodiment, a fine integration method of the fuzzy fractional order ordinary differential equation of the pad series approximation is first derived. The matrix index EA (p, q) of the fuzzy fractional order ordinary differential equation using the pad approximation is defined as:
wherein the content of the first and second substances,
by using the property of matrix index, the matrix weighting index A'/2 n is closeSimilar to the number of stages (p, g) for the series of pad operations, an approximation of the matrix index EA can be obtained. Wherein, the approximate value of the matrix index EA can be expressed as fpq (a), and the equation expression is:
in this expression, the matrix weighting index a' is a/2n, where n is referred to as the weighting coefficient of matrix a, (p, q) is the number of the expansion terms from the pad array, and the pad may be similarly diagonal, assuming p is q.
For the weighting matrix A, after fine segmentation, approximate N is calculated using the pad series approximation seriespq(A'), in calculating DpqIn the process of (A'), rounding errors increase and the value becomes unstable. Therefore, in this embodiment, the delta storage technique is used to separate the delta cells of the Q-diagonal pad array used to approximate a', which is expressed as:
from these three formulas, it can be calculated: ra＝(I+Da)-1(Na-Da). Calculating the initial increment of the above equation requires solving the inverse matrix (I + Da). Considering that Da of the subdivided weighting matrix a' is a small quantity, the matrix (I + Da) has a distinct diagonal matrix advantage and has good numerical characteristics, and thus does not cause numerical stability problems.
After the superposition principle and the incremental storage are used for carrying out multiple times of calculation, a matrix index based on the pade array approximation can be obtained: fpq(A)＝I+Ra。
In an embodiment, the provided data transmission method may further include a process of obtaining an adaptive selection optimization formula, where the specific process includes: obtaining the relation between the matrix weighting index and the matrix expansion item; calculating the relative error between the matrix weighting index and the matrix expansion item according to the relation; and obtaining a self-adaptive selection optimization formula according to the relative error.
The matrix weighting index and the matrix expansion term are two factors influencing the approximation increment precision of the pad array. The computer equipment can obtain the relation between the matrix weighting index and the matrix expansion item, so that the relative error between the matrix weighting index and the matrix expansion item is calculated according to the relation, and the self-adaptive selection optimization formula is obtained.
The precision of the matrix equation index is mainly influenced by the precision of the similarity value of the initial increment, so that the increment precision of the initial pad array approximation can be improved by adding the weighting parameter and the expansion term, but the calculation amount is increased. At present, most studies analyze and select weighting parameters under the condition of given expansion terms, but the combination of (N, q) obtained only by experience cannot ensure the calculation amount of the algorithm to be minimum. In this embodiment, at a specified EPS accuracy, the relationship between the weighting parameter N and the expansion term q may be defined as:
. In this relation, the required relative error accuracy is expressed as EPS, and Err (N, q) is the actual calculated relative error. The computer device can obtain an adaptive selection optimization formula according to the relative error.
In another embodiment, the provided output transmission method may further include a process of obtaining an adaptive selection optimization formula, where the specific process includes: obtaining a first change formula of a matrix weighting index according to the relative error, and obtaining a second change formula of a matrix expansion item according to the relative error; and obtaining a self-adaptive selection optimization formula according to the first variation formula and the second variation formula. The self-adaptive selection optimization formula comprises a matrix weighting index self-adaptive selection optimization formula and a matrix expansion term self-adaptive selection optimization formula.
After the relative error is obtained, the computer equipment can control the matrix expansion item to be unchanged and increase the matrix weighting index, so that a first change formula of the matrix weighting index is obtained; similarly, the computer device may control the matrix weighting index to be constant and the matrix expansion term to be increased, thereby obtaining a second variation formula of the matrix expansion term. The computer equipment can obtain a self-adaptive selection optimization formula of the matrix weighting index according to the first change formula; and obtaining a self-adaptive selection optimization formula of the matrix expansion term according to the second variation formula.
When the matrix weighting index N or the matrix expansion term q is determined, the change of the matrix expansion term q may be analyzed first and then the influence of the matrix weighting index N on the error accuracy may be analyzed. First, q remains unchanged and N increases, the first variation formula for obtaining the matrix weighting index is:
receiving, keeping N unchanged, increasing q, and obtaining a second variation formula of a matrix expansion term as follows:
in this embodiment, the first variation formula and the second variation formula may be used to represent the influence of the matrix weighting index N and the matrix expansion term q variation on the accuracy of the relative error, and may be obtained
I.e. any change in N or q will increase the accuracy of the relative error.
In the present embodiment, ρN(N, q) is a function that improves the accuracy of the relative error, N is exponential, and ρ isq(N, q) is a function that improves the accuracy of the relative error, q being the power of q. When the initial value q is 1, ρ isN(N,1)<ρq(N, 1). Exponential function ρ with increasing qNThe drop is faster. Therefore, q (N) must be present>1, this will result in q>q (N) and ρN(N,q*)<ρq(N,q*). Thus, q × N, ρ is presentN(N,q*)＝ρq(N,q*) The computer device may derive a rule to increase N or q: when q is less than or equal to q (N), increasing the number q of expansion terms; when g is>q (N), the weighting parameter N needs to be increased until the specified accuracy is reached, as shown in fig. 3. The computer device may obtain an adaptive selection optimization formula:
ρN(N+1,q)＝ρN(N,q)，
when q is q0＝1，N＝N0As an initial value, when the relative error accuracy requirement is met, the equation can be obtained:
in an embodiment, the provided data transmission method may further include a data transmission process, where the specific process includes: dividing the data to be transmitted according to the total calculated amount to obtain each transmission subdata; distributing task nodes for each transmission subdata through an API (application programming interface) of an SDN (software defined network) architecture; and carrying out data transmission through the task node.
The computer device may divide the data to be transmitted into the respective transmission sub-data according to the total calculation amount. The SDN network architecture provides an API (application programming interface) for upper-layer application, so that the automatic control capability of the network can be improved, and new network application can be operated more easily. A task manager in the SDN network architecture may allocate task nodes to each transmission sub-data, and transmit the data through the task nodes.
In one embodiment, as shown in fig. 4, the SDN network architecture is a network control framework, which can manage the operation of the whole network, and has strong flexibility, and at the same time, improves the efficiency of big data computation. The operation functions of the SDN network architecture are mainly divided into a network management function and a network forwarding function, the operation of the control module and the operation of the data module are separated, and the operation efficiency of the whole system network is greatly improved.
In an embodiment, the provided data transmission method may further include a process of performing data transmission by the task node, and specifically includes: processing the data in the task node by an open flow technology to obtain processed data; acquiring an openflow controller corresponding to the openflow technology; and carrying out data transmission on the processed data through the open flow controller.
The open flow technology is a key technology of the SDN and is a first interface connecting a control module and a data module in the whole framework. The openflow is composed of a control center and an openswitch, as shown in fig. 5, the control center is a control core in the whole SDN operation process, and the openflow switch is mainly responsible for forwarding of flow data. In the open flow, node information between the respective modules is exchanged using a general flow table. And remote access and monitoring are carried out through an open stream interface protocol. Meanwhile, by opening the API interfaces of the south side and the north side, the network can be seamlessly connected with various requirements such as delay, bandwidth, charging and the like, so that the whole network has programmability. The programmable interfaces can call resources and develop network services, shorten the online period of new services and promote the development of networks.
In one embodiment, as shown in fig. 6, an information application system may be established on the basis of the SDN network architecture, and the information application system is divided into a control module and a data transmission module, and the information application system submits tasks to be performed and data to be processed through a client, and manages work tasks in nodes by using a task manager.
In the information application system, a control module is the brain of the whole system and is responsible for task scheduling of the whole system, the operation mode can be adjusted, the operation mode of the open flow network is flexibly adjusted through the control module, and the control module and the open flow network cooperate with each other to fully utilize data resources in the network. The whole task operation scheduling process is composed of a running tracking node and a controller in an open flow, and the whole task is scheduled by a mechanism of pulling task computing resources to a scheduler as shown in fig. 7. The operation tracker has a fault tolerance mechanism, so when the task handed over by one node has an operation problem, the operation tracker is transferred to other nodes to be operated again. The openflow controller can be connected with the operation tracker node at the same frequency. The controller sends a query information request to the operation tracking node at regular time to query whether a new task scheduling assignment exists. When available resources appear, a new task can be pulled to the node to run. When the run tracker node resumes a task, the node's IP and the task ID of the task executing on the node are sent to the OpenFlow controller together.
As shown in fig. 8, the data transfer module of the information application system corresponds to a task executor in the entire system. After receiving the receipt set flow at the system port, the data is loaded into the buffer through the OpenFlow switch, and the data in the buffer is processed. If the packet does not match its flow entry table, the packet is defined as an unknown packet. It sends the data to the controller, which then makes the corresponding routing operation on the unknown data, which is how the OpenFlow switch handles the unknown data. And if the corresponding flow table is matched, running the corresponding task according to the flow table. Either the data tasks are executed directly or different types of tasks are added to the task set.
It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in the above-described flowcharts may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or the stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, the scheme is tested, and the test process and the result are as follows, two examples are selected in the test, and the correctness and the effectiveness of the self-adaptive selection method are verified through a matrix weighting index N and a matrix expansion item q which are obtained through calculation on a MAT L AB platform.
Matrix construction of the first example:
wherein m is>n, then exp (a) is calculated. The eigenvalues of the matrix are: eig (a) ═ 1, - (m-n)]. Obtaining an analytic solution by a characteristic point analytic method:
from the eigenvalues of the matrix, when m>n, the stiffness of the matrix is increasing, so it can be used to check the performance of the algorithm: when n is 2, m is 20,21,22,23, …, 25, the rigidity of the matrix increases gradually as m increases. Parameters (N, q) before and after improvement are adaptively selected and calculated with accuracy fixed in EPS 10-12, and are compared with the analytical solutions, respectively. As shown in fig. 9 below, it can be seen that the numerical precision of the improved algorithm and the error of the analytic solution remain substantially unchanged at different matrix stiffness, while the error values of the improved algorithm and the analytic solution increase as the matrix stiffness increases. In addition, the numerical precision of the improved algorithm and the error of the analytic solution are always smaller than those of the algorithm before the improvement.
The second is that random dimensions B, n, 60, 350 are given to the matrix at a specified accuracy EPS 10-14, respectively, the computation time of the algorithm before and after optimization is compared on the MAT L AB platform, as shown in fig. 10, the computation time required to improve the algorithm is not significantly different from the algorithm before improvement (P >0.05) when the dimensions are 60, 120 and 180, respectively, and is significantly reduced (P <0.05) when the dimensions are 240, 300 and 360, respectively.
As can be seen from the first and second examples, the improved fine integration algorithm performs much better than the improved algorithm, both in numerical precision and in the high dimension. It also shows that the fractional order fuzzy differential equation of the improved fine integration method is practical to find the matrix weighting index N and the matrix expansion term q by the adaptive selection method.
The experiment also includes verification of the bandwidth optimized information application system. The test is carried out on a real data center platform, the Open Flow Vswitch runs on an Open Flow switch machine platform, and the Open Flow controller runs on an Open Daylight platform. In order to detect the bandwidth optimization performance of the system, the flow size of a system port and the fusion rate of data under the condition of inputting different flow data are detected through two groups of experiments. And the time required to complete the system tasks under different bandwidths.
This study was tested using the mission module of the system. In the original information application system, along with the loading of tasks, the size of port data streams and the data fusion rate are continuously increased. When the input flow is respectively 800 m, 1600 m, 6000 m, 14000 m and 2000m, the load of the tasks is sequentially increased, and the size of the port data stream and the data fusion rate of the improved system represent the performance of the system. It shows that the smaller the port data volume, the higher the merging rate, and the better the system performance. As shown in fig. 11, where a represents the amount of data of the port when the flow value of the system after improvement is different from that of the system before improvement; and B is the port data merging rate of the improved system and the improved system. Although the port data flow of both the improved system and the system before improvement increases with the increase of the input stream data, the improved system port data flow is gradually smaller than the system port data flow before improvement. And the gap is expanding. The improved system is significantly higher than the system before the improvement in terms of data fusion rate.
In a second experiment, a task module of the system was targeted for the test sample. Under the condition that the flow of input data is fixed at 15000M, the port bandwidth of the Open Vswitch is converted from small to large and is respectively 20M, 60M and 120M, and the time required by two information application systems to complete tasks under different bandwidth conditions is detected and recorded. The time required to complete a task directly represents the operating efficiency of the system. The shorter the required time, the more efficient the system will operate. As shown in fig. 12 below, the time required for the modified system to complete a task when the system is operating at 20M and 60M bandwidth is much less than the time it takes for the system before modification. At 120M bandwidth, the time required for the system task to complete after improvement is less than that of the system before improvement.
In one embodiment, as shown in fig. 13, there is provided a data transmission system including: a matrix index extraction module 1310, a target matrix index acquisition module 1320, a formula acquisition module 1330, a calculation module 1340, and a data transmission module 1350, wherein:
the matrix index extraction module 1310 is configured to obtain the fuzzy fractional order ordinary differential equation and extract a matrix index of the fuzzy fractional order ordinary differential equation.
The target matrix index obtaining module 1320 is configured to optimize the matrix index by using a fine integration method to obtain a target matrix index, and obtain a matrix weighting index and a matrix expansion term corresponding to the target matrix index.
The formula obtaining module 1330 is configured to obtain the adaptive selection optimization formula according to the matrix weighting index and the matrix expansion term.
The calculating module 1340 is configured to obtain data to be transmitted, and obtain a total calculation amount of the adaptive selection optimization formula according to the data to be transmitted.
The data transmission module 1350 is configured to transmit data to be transmitted through an SDN network architecture according to the total calculated amount.
In one embodiment, the target matrix index obtaining module 1320 includes an inverse matrix calculation module, an index processing module, and an approximation obtaining module, where:
and the inverse matrix calculation module is used for calculating an inverse matrix of the matrix corresponding to the matrix index.
And the index processing module is used for carrying out pad series approximation on the matrix index through an increment storage technology to obtain a matrix weighting index.
And the approximate value acquisition module is used for obtaining an approximate value of the matrix index according to the inverse matrix and the matrix weighting index by using a fine integration method, and taking the approximate value of the matrix index as the target matrix index.
In one embodiment, the formula obtaining module 1330 is further configured to obtain a relationship between the matrix weighting index and the matrix expansion term; calculating the relative error between the matrix weighting index and the matrix expansion item according to the relation; and obtaining a self-adaptive selection optimization formula according to the relative error.
In one embodiment, the formula obtaining module 1330 is further configured to obtain a first variation formula of the matrix weighting index according to the relative error, and obtain a second variation formula of the term according to the relative error; obtaining a self-adaptive selection optimization formula according to the first change formula and the second change formula; the self-adaptive selection optimization formula comprises a matrix weighting index self-adaptive selection optimization formula and a matrix expansion term self-adaptive selection optimization formula.
In one embodiment, the data transmission module 1350 is further configured to divide the data to be transmitted according to the total calculated amount to obtain each transmission subdata; distributing task nodes for each transmission subdata through an API (application programming interface) of an SDN (software defined network) architecture; and carrying out data transmission through the task node.
In an embodiment, the data transmission module 1350 is further configured to process the data in the task node through an open flow technique to obtain processed data; acquiring an openflow controller corresponding to the openflow technology; and carrying out data transmission on the processed data through the open flow controller.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 14. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data transmission method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 14 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
and acquiring a fuzzy fractional order ordinary differential equation, and extracting a matrix index of the fuzzy fractional order ordinary differential equation.
And optimizing the matrix index by using a fine integration method to obtain a target matrix index, and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index.
And obtaining a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion item.
And acquiring data to be transmitted, and acquiring the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted.
And transmitting the data to be transmitted through an SDN network architecture according to the total calculated amount.
In one embodiment, the processor, when executing the computer program, further performs the steps of: calculating an inverse matrix of a matrix corresponding to the matrix index; carrying out pad series approximation on the matrix index by an incremental storage technology to obtain a matrix weighting index; and obtaining an approximate value of the matrix index by using a fine integration method according to the inverse matrix and the matrix weighting index, and taking the approximate value of the matrix index as the target matrix index.
In one embodiment, the processor, when executing the computer program, further performs the steps of: obtaining the relation between the matrix weighting index and the matrix expansion item; calculating the relative error between the matrix weighting index and the matrix expansion item according to the relation; and obtaining a self-adaptive selection optimization formula according to the relative error.
In one embodiment, the processor, when executing the computer program, further performs the steps of: obtaining a first change formula of a matrix weighting index according to the relative error, and obtaining a second change formula of a matrix expansion item according to the relative error; obtaining a self-adaptive selection optimization formula according to the first change formula and the second change formula; the self-adaptive selection optimization formula comprises a matrix weighting index self-adaptive selection optimization formula and a matrix expansion term self-adaptive selection optimization formula.
In one embodiment, the processor, when executing the computer program, further performs the steps of: dividing the data to be transmitted according to the total calculated amount to obtain each transmission subdata; distributing task nodes for each transmission subdata through an API (application programming interface) of an SDN (software defined network) architecture; and carrying out data transmission through the task node.
In one embodiment, the processor, when executing the computer program, further performs the steps of: processing the data in the task node by an open flow technology to obtain processed data; acquiring an openflow controller corresponding to the openflow technology; and carrying out data transmission on the processed data through the open flow controller.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
and acquiring a fuzzy fractional order ordinary differential equation, and extracting a matrix index of the fuzzy fractional order ordinary differential equation.
And optimizing the matrix index by using a fine integration method to obtain a target matrix index, and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index.
And obtaining a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion item.
And acquiring data to be transmitted, and acquiring the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted.
And transmitting the data to be transmitted through an SDN network architecture according to the total calculated amount.
In one embodiment, the computer program when executed by the processor further performs the steps of: calculating an inverse matrix of a matrix corresponding to the matrix index; carrying out pad series approximation on the matrix index by an incremental storage technology to obtain a matrix weighting index; and obtaining an approximate value of the matrix index by using a fine integration method according to the inverse matrix and the matrix weighting index, and taking the approximate value of the matrix index as the target matrix index.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining the relation between the matrix weighting index and the matrix expansion item; calculating the relative error between the matrix weighting index and the matrix expansion item according to the relation; and obtaining a self-adaptive selection optimization formula according to the relative error.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining a first change formula of a matrix weighting index according to the relative error, and obtaining a second change formula of a matrix expansion item according to the relative error; obtaining a self-adaptive selection optimization formula according to the first change formula and the second change formula; the self-adaptive selection optimization formula comprises a matrix weighting index self-adaptive selection optimization formula and a matrix expansion term self-adaptive selection optimization formula.
In one embodiment, the computer program when executed by the processor further performs the steps of: dividing the data to be transmitted according to the total calculated amount to obtain each transmission subdata; distributing task nodes for each transmission subdata through an API (application programming interface) of an SDN (software defined network) architecture; and carrying out data transmission through the task node.
In one embodiment, the computer program when executed by the processor further performs the steps of: processing the data in the task node by an open flow technology to obtain processed data; acquiring an openflow controller corresponding to the openflow technology; and carrying out data transmission on the processed data through the open flow controller.
It will be understood by those of ordinary skill in the art that all or a portion of the processes of the methods of the embodiments described above may be implemented by a computer program that may be stored on a non-volatile computer-readable storage medium, which when executed, may include the processes of the embodiments of the methods described above, wherein any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of data transmission, the method comprising:
acquiring a fuzzy fractional order ordinary differential equation, and extracting a matrix index of the fuzzy fractional order ordinary differential equation;
optimizing the matrix index by using a fine integration method to obtain a target matrix index, and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index;
obtaining a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion item;
acquiring data to be transmitted, and acquiring the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted;
and transmitting the data to be transmitted through an SDN network architecture according to the total calculated amount.
2. The method of claim 1, wherein the optimizing the matrix index using a fine integration method to obtain a target matrix index comprises:
calculating an inverse matrix of a matrix corresponding to the matrix index;
carrying out pad series approximation on the matrix index by an incremental storage technology to obtain a matrix weighting index;
and obtaining an approximate value of the matrix index according to the inverse matrix and the matrix weighting index by using the fine integration method, and taking the approximate value of the matrix index as the target matrix index.
3. The method of claim 1, wherein obtaining an adaptive selection optimization formula according to the matrix weighting index and the matrix expansion term comprises:
obtaining the relation between the matrix weighting index and the matrix expansion item;
calculating the relative error between the matrix weighting index and the matrix expansion item according to the relation;
and obtaining the self-adaptive selection optimization formula according to the relative error.
4. The method of claim 3, wherein deriving the adaptive selection optimization formula based on the relative error comprises:
obtaining a first change formula of the matrix weighting index according to the relative error, and obtaining a second change formula of the matrix expansion item according to the relative error;
obtaining the self-adaptive selection optimization formula according to the first variation formula and the second variation formula;
the adaptive selection optimization formula comprises an adaptive selection optimization formula of the matrix weighting index and an adaptive selection optimization formula of the matrix expansion term.
5. The method of claim 1, wherein the transmitting the data to be transmitted through an SDN network architecture according to the total calculated amount comprises:
dividing the data to be transmitted according to the total calculated amount to obtain each transmission subdata;
distributing task nodes for the transmission subdata through an API (application programming interface) of the SDN network architecture;
and carrying out data transmission through the task node.
6. The method of claim 5, wherein the transmitting data by the task node comprises:
processing data in the task node through an open flow technology to obtain processed data;
acquiring an openflow controller corresponding to the openflow technology;
and carrying out data transmission on the processed data through the open flow controller.
7. A data transmission system, the system comprising:
the matrix index extraction module is used for acquiring a fuzzy fractional order ordinary differential equation and extracting a matrix index of the fuzzy fractional order ordinary differential equation;
the target matrix index acquisition module is used for optimizing the matrix index by using a fine integration method to obtain a target matrix index and acquiring a matrix weighting index and a matrix expansion item corresponding to the target matrix index;
the formula acquisition module is used for acquiring a self-adaptive selection optimization formula according to the matrix weighting index and the matrix expansion item;
the calculation module is used for acquiring data to be transmitted and obtaining the total calculation amount of the self-adaptive selection optimization formula according to the data to be transmitted;
and the data transmission module is used for transmitting the data to be transmitted through an SDN network architecture according to the total calculated amount.
8. The system of claim 7, wherein the target matrix index acquisition module comprises:
the inverse matrix calculation module is used for calculating an inverse matrix of a matrix corresponding to the matrix index;
the index processing module is used for carrying out pad series approximation on the matrix index through an increment storage technology to obtain a matrix weighting index;
and the approximate value acquisition module is used for obtaining an approximate value of the matrix index according to the inverse matrix and the matrix weighting index by using the fine integration method, and taking the approximate value of the matrix index as the target matrix index.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102664397A (en) * | 2012-03-23 | 2012-09-12 | 浙江大学 | Electric power system transient stability simulation method based on implicit fine numerical integral |
| CN103139265A (en) * | 2011-12-01 | 2013-06-05 | 国际商业机器公司 | Network transmission self-adaption optimizing method and system in large-scale parallel computing system |
| CN105468864A (en) * | 2015-12-14 | 2016-04-06 | 三峡大学 | Electromagnetic transient numerical computation method of high-voltage power transmission line based on increment dimension precise integration |
| CN110008635A (en) * | 2019-04-19 | 2019-07-12 | 陕西新西商工程科技有限公司 | Using Newmark precise integration combined techniques to the method for Elasto-Plastic Structures seismic response analysis |
| CN110188311A (en) * | 2019-04-23 | 2019-08-30 | 南京航空航天大学 | High-speed machining stable region prediction technique based on cutter tooth cutting Time precision integration |
| CN110826881A (en) * | 2019-10-25 | 2020-02-21 | 北京控制工程研究所 | Spacecraft on-orbit health state assessment method and system considering uncertain interference |
-
2020
- 2020-03-13 CN CN202010177134.6A patent/CN111404825A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103139265A (en) * | 2011-12-01 | 2013-06-05 | 国际商业机器公司 | Network transmission self-adaption optimizing method and system in large-scale parallel computing system |
| CN102664397A (en) * | 2012-03-23 | 2012-09-12 | 浙江大学 | Electric power system transient stability simulation method based on implicit fine numerical integral |
| CN105468864A (en) * | 2015-12-14 | 2016-04-06 | 三峡大学 | Electromagnetic transient numerical computation method of high-voltage power transmission line based on increment dimension precise integration |
| CN110008635A (en) * | 2019-04-19 | 2019-07-12 | 陕西新西商工程科技有限公司 | Using Newmark precise integration combined techniques to the method for Elasto-Plastic Structures seismic response analysis |
| CN110188311A (en) * | 2019-04-23 | 2019-08-30 | 南京航空航天大学 | High-speed machining stable region prediction technique based on cutter tooth cutting Time precision integration |
| CN110826881A (en) * | 2019-10-25 | 2020-02-21 | 北京控制工程研究所 | Spacecraft on-orbit health state assessment method and system considering uncertain interference |
Non-Patent Citations (1)
| Title |
|---|
| 谭述君: "精细积分方法的改进及其在动力学与控制中的应用", 《中国博士学位论文全文数据库》 * |
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