CN112804702B - Multi-link air-ground data exchange link performance evaluation method based on utility function - Google Patents

Abstract

The invention relates to a multi-link air-ground data exchange link performance evaluation method based on a utility function, and belongs to the field of air-ground broadband communication. The method includes: S1: collect performance indicators of each link, and matrix the indicators to construct a decision matrix; then, according to the business flow, introduce fuzzy theory in fuzzy mathematics to construct a fuzzy positive and complementary judgment matrix; Calculate the index weight with the fuzzy positive and complementary judgment matrix to obtain the objective weight and subjective weight of each index; finally combine the objective weight and subjective weight to obtain the subjective and objective comprehensive weight; S2: Adjust according to the sensitivity of the business to the index The parameters of the utility function are changed, the graph of the utility function is changed to meet the needs of different services for the indicators, the decision matrix is processed, and finally the final link total utility value is obtained by multiplying the aggregated utility. The present invention improves the accuracy of the algorithm for evaluating the network performance based on multiple links under multiple attributes.

Description

Link performance evaluation method for multi-link air-ground data exchange based on utility function

technical field

The invention belongs to the field of air-ground broadband communication, and relates to a method for evaluating the link performance of a multi-link air-ground data exchange based on a utility function.

Background technique

With the rapid development of aerospace communication technology, ground-air broadband communication has become a reality, and ground-air communication has also begun to enter civil aviation. At present, there are three main modes of providing in-flight Internet access in civil aviation:

(1) The use of satellite links to establish a connection with the ground network, that is, satellite communication technology, is a solution to realize air-ground broadband wireless communication at present.

(2) ATG technology. Air-to-Ground broadband access system based on ground base station, its main working principle is that the land system deploys a dedicated base station to establish a communication link with the ATG equipment built on the aircraft, so as to connect to the network.

(3) Airborne wireless local area network: that is, use a terminal such as a tablet computer or a laptop computer to log in to the airborne local area network and browse the local resources in the airborne local area network.

Network service access control is the basis for providing good QoS (Quality of Service). It determines the accessibility of the network under resource availability, thereby avoiding network congestion and service quality degradation for online users. It has become the trend of future network access to ensure that users can quickly select and access the optimal network from the wireless network to improve user service quality. The link performance evaluation is the most direct decision basis for selecting link access.

At present, there are many link evaluation methods used in academia, including Entropy Weight Method (EWM), Analytic Hierarchy Process (AHP), and distance between superior and inferior solutions (TOPSIS). For example: the patent application "Grey Correlation and Fuzzy Evaluation Method and System of Multipath Transmission Network Performance" (Publication No.: CN110912768A) firstly obtains the multipath transmission network information to be evaluated, adopts the grey correlation analysis method and the fuzzy comprehensive analysis method according to the described Network quantity information, the network evaluation index information, establish a network performance evaluation model, and calculate the membership degree vector value of each evaluation comment corresponding to each network in the multi-path network, and finally select the largest corresponding membership degree vector value. The evaluation comments are the final evaluation results of each network, but this solution is not applicable to services with different service characteristics. The patent "A Service-Oriented Network Comprehensive Performance Evaluation Method" (publication number: CN105207821A) uses low-complexity FAHP to process performance parameters, calculates their weight vectors, and then measures the network parameter values of the current network in real time. , the fuzzy membership function is introduced to calculate the score corresponding to the measured value of each network parameter, and each score is weighted to obtain the performance evaluation index result of the service, but the weight design of this scheme is too subjective.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a kind of multi-link air-ground data exchange link performance evaluation method based on utility function, solve the network performance of evaluating multi-link multi-attribute and the subjective judgment produced by artificial subjective weighting is too heavy. The problem is to improve the accuracy of the algorithm for multi-attribute-based multi-link network performance evaluation.

To achieve the above object, the present invention provides the following technical solutions:

A method for evaluating the performance of a multi-link air-ground data exchange link based on a utility function, comprising the following steps:

S1: Collect the performance indicators of each link, and matrix the indicators to construct a decision matrix; then, according to the business flow, introduce fuzzy theory in fuzzy mathematics to construct a fuzzy positive and complementary judgment matrix; The judgment matrix is used to calculate the index weight, and the objective weight and subjective weight of each index are obtained; finally, the objective weight and subjective weight are combined to obtain the subjective and objective comprehensive weight;

S2: Adjust the parameters of the utility function and change the graph of the utility function according to the different sensitivities of the business to the indicators, so as to adapt to the requirements of different businesses for the indicators, process the decision matrix, and finally obtain the final link total by multiplying the aggregated utility. Utility value, select the link access with the maximum utility value.

Further, the step S1 specifically includes the following steps:

S11: Collect performance indicators of each link, including link bandwidth, delay, packet loss rate, and delay jitter;

S12: Establish a decision matrix A according to the performance indicators of each link. Since both delay and packet loss rate are cost-based indicators, they need to be forwarded and converted into benefit-based indicators. In order to eliminate the dimension between indicators The influence of each index should be standardized, and then the proportion of each index in each link should be calculated to obtain the probability matrix P;

S13: Calculate the information entropy of each index, and judge the discrete degree of each index. The smaller the information entropy value, the greater the discrete degree of the index and the greater the weight. Calculate the information entropy of each index through the probability matrix, and then calculate the information utility value, and then normalized to obtain the objective weight w ₀ of each indicator;

S14: By introducing the fuzzy theory in fuzzy mathematics, the fuzzy matrix is the matrix representation of the fuzzy relationship. According to the fuzzy relationship on the universe U={b ₁ ,b ₂ ,...,b _n } "one index is more important than another index. degree”, establish a fuzzy positive and complementary judgment matrix B;

S15: Normalize the fuzzy positive and complementary judgment matrix into a fuzzy consistent matrix; then use the eigenroot method to sum up the elements of each row of the matrix, and then normalize to obtain the weight of a single layer, that is, to obtain the value of each index subjective weight w ₁ ;

S16: Combine the objective weights and subjective weights obtained in steps S13 and S14, and dynamically adjust the ratio of subjective and objective weights according to the actual needs of user services to obtain comprehensive weights:

w=αw ₀ +(1-α)w ₁

Among them, α is a parameter for dynamically adjusting the subjective and objective weight ratio.

Further, in step S13, the calculation formula of the information utility value is:

Among them, e _j represents the information entropy of the jth indicator, m is the total number of indicators, and n is the total number of links.

Further, in step S2, the expression of the utility function is:

Among them, a and b are the parameters of the utility function respectively;

Construct its appropriate utility function u _j (x) for each attribute, and obtain the corresponding utility value matrix through the utility function:

Then multiply the aggregated utility function to obtain the total utility value of the link, and the network with the largest utility value is the selected network;

Among them, U(x) is the total utility value of the link, _wi is the subjective and objective comprehensive weight of the ith indicator, and m is the total number of indicators for each link.

Further, in step S2, according to the different sensitivities of the business to the index, the values of parameter a and parameter b are adjusted to make the function better adapt to the business characteristics within a certain range of the index value, making it more flexible, and then corresponding to each index. The utility function processes the decision matrix to obtain the corresponding utility value matrix.

The beneficial effects of the present invention are as follows: the present invention obtains the subjective and objective weight vectors of indicators respectively by introducing the definition of information entropy in information theory and the fuzzy theory in fuzzy mathematics, and then according to the utility function of each indicator under each business, Finally, the final utility value of each link obtained by multiplying the aggregated utility completely characterizes the overall performance of each link. The invention improves the accuracy of the algorithm for evaluating the network performance based on multiple links under multiple attributes.

Other advantages, objects, and features of the present invention will be set forth in the description that follows, and will be apparent to those skilled in the art based on a study of the following, to the extent that is taught in the practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the following description.

Description of drawings

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be preferably described in detail below with reference to the accompanying drawings, wherein:

1 is a flowchart of a method for evaluating the performance of a multi-link air-ground data exchange link based on a utility function of the present invention;

Figure 2 is a schematic diagram of the framework of the hierarchical model.

Detailed ways

The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments are only used to illustrate the basic idea of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

Please refer to FIG. 1 to FIG. 2. FIG. 1 is a flowchart of a preferred method for evaluating the link performance of a multi-link air-ground data exchange based on a utility function. Air-ground data exchange requires multiple links, and each link has multiple attributes, and each link index has its own properties and characteristics. If the judgment of each link index is subjective, the results will be very different. Therefore, in the present invention, by introducing the definition of information entropy in information theory and the fuzzy theory in fuzzy mathematics, the subjective and objective weight vectors of the indicators are obtained respectively, and then according to the utility function of each indicator under each business, finally through The final utility value of each link obtained by multiplying the aggregated utility completely characterizes the overall performance of each link. The method specifically includes the following steps:

S1: Collect parameter indicators of each link, including link packet loss rate, delay, bandwidth utilization, and delay jitter, and establish a decision matrix A, as shown in equation (1):

Among them, n is the total number of links, m is the total number of indicators, and the value of the matrix element a _ij (i=1,2,...,n; j=1,2,...,m) is a positive value greater than 0 number, indicating the actual value of each indicator. The objective constant weight w ₀ of each index is obtained by introducing information entropy.

S2: For various services, such as session services, the bandwidth requirements are low, but the latency requirements are high. High latency will affect the smoothness of communication and thus the user's service experience. Therefore, in this type of service, the latency The proportion is the highest. According to this idea, the subjective attribute judgment matrix is constructed, and the subjective weight w ₁ is obtained by introducing the fuzzy theory.

S3: According to formula (2), the subjective and objective weights w are obtained.

w=αw ₀ +(1-α)w ₁ (2)

S4: Construct an appropriate utility function for each index. This method selects the sigmoid function as the utility function, as shown in formula (3):

And according to the business characteristics, the utility function is set for each indicator, and by changing the shape of the function graph, the utility function can adapt to different business needs.

S5: Construct a suitable utility function u _j (x) for each attribute, and obtain the corresponding utility value matrix through the utility function:

S6: Aggregate the utility function through cumulative multiplication, such as formula (5):

Finally, the total utility value of the link is obtained, and the network with the largest utility value is the selected network.

Step S1 specifically includes:

1) After forwarding and normalizing the decision matrix A in step S1, calculate the proportion of the i-th sample under the j-th index, and regard it as the probability used in the relative entropy calculation. After the previous step Process to get a normalized matrix:

Calculate the probability matrix P, where the calculation formula of each element p _ij in P is shown in formula (7):

即保证了每个指标所对应的概率和为1。in,

That is, the probability sum corresponding to each indicator is guaranteed to be 1.

2) After obtaining the probability matrix, then calculate the information entropy of each index, calculate the information utility value, and normalize the entropy weight of each index. For the jth index, the information entropy is calculated as formula ( 8) shown:

Definition of Information Utility Value:

d _j =1-e _j (9)

By normalizing the information utility value, the entropy weight of each indicator can be obtained, that is, the constant weight of each indicator:

Step S2 specifically includes: starting from multiple attributes, constructing a hierarchical model: goals, criteria and solutions, as shown in FIG. 2 .

1) According to the scaling method defined in Table 1 below, evaluate the relative importance of the indicators to form a fuzzy positive and complementary judgment matrix B:

Table 1 Importance scale method

Among them, the judgment matrix B has the following properties:

b _ij > 0 (12)

b _ii =b _jj =0.5 (13)

b _ij + b _ji = 1 (14)

2) Normalize the fuzzy positive and complementary judgment matrix into a fuzzy consistent matrix, then use the characteristic root method to sum each row of the matrix, and then normalize to obtain the weight of a single layer, that is, to obtain the value of each index. Subjective weight w ₁ .

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements, without departing from the spirit and scope of the technical solution, should all be included in the scope of the claims of the present invention.

Claims (3)

1. a multi-link air-ground data exchange link performance evaluation method based on utility function, is characterized in that, this method may further comprise the steps: S1: Collect the performance indicators of each link, and matrix the indicators to construct a decision matrix; then, according to the business flow, introduce fuzzy theory in fuzzy mathematics to construct a fuzzy positive and complementary judgment matrix; The judgment matrix is used to calculate the index weight, and the objective weight and subjective weight of each index are obtained; finally, the objective weight and subjective weight are combined to obtain the subjective and objective comprehensive weight; S2: Adjust the parameters of the utility function and change the graph of the utility function according to the different sensitivities of the business to the indicators, so as to adapt to the requirements of different businesses for the indicators, process the decision matrix, and finally obtain the final link total by multiplying the aggregated utility. Utility value, select the link access with the maximum utility value; The expression of the utility function is:

Among them, a and b are the dynamically adjustable parameters of the utility function, respectively; Construct its appropriate utility function u _j (x) for each attribute, and obtain the corresponding utility value matrix through the utility function:

Then multiply the aggregated utility function to obtain the total utility value of the link, and the network with the largest utility value is the selected network;

Among them, U(x) is the total utility value of the link, _wi is the subjective and objective comprehensive weight of the ith indicator, and m is the total number of indicators for each link. 2. The multi-link air-ground data exchange link performance evaluation method according to claim 1, wherein the step S1 specifically comprises the following steps: S11: Collect performance indicators of each link, including link bandwidth, delay, packet loss rate, and delay jitter; S12: Establish a decision matrix A according to the performance indicators of each link, standardize each indicator, and then calculate the proportion of each indicator in each link to obtain a probability matrix P; S13: Calculate the information entropy of each index, then calculate the information utility value, and then normalize to obtain the objective weight w ₀ of each index; S14: By introducing the fuzzy theory in fuzzy mathematics, the fuzzy matrix is the matrix representation of the fuzzy relationship, and according to the fuzzy relationship on the universe U={b ₁ ,b ₂ ,...,b _n }, establish the fuzzy positive and complementary judgment matrix B; S15: Normalize the fuzzy positive and complementary judgment matrix into a fuzzy consistent matrix; then use the eigenroot method to sum up the elements of each row of the matrix, and then normalize to obtain the weight of a single layer, that is, to obtain the value of each index subjective weight w ₁ ; S16: Combine the objective weights and subjective weights obtained in steps S13 and S15, and dynamically adjust the ratio of subjective and objective weights according to the actual needs of user services to obtain comprehensive weights: w=αw ₀ +(1-α)w ₁ Among them, α is a parameter for dynamically adjusting the subjective and objective weight ratio. 3. multi-link air-ground data exchange link performance evaluation method according to claim 1, is characterized in that, in step S2, according to the different sensitivities of service to index, adjust the value of parameter a and parameter b so that the function is in the index. The value is suitable for business characteristics within a certain range, and then the decision matrix is processed through the utility function corresponding to each index to obtain the corresponding utility value matrix.

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