Ship-end dynamic routing control method based on dynamic characteristics of service and wireless channel

Abstract

The invention discloses a ship-end dynamic routing control method based on dynamic characteristics of a service and a wireless channel, and belongs to the technical field of ship communication. The method comprises the following steps: constructing a fuzzy hierarchical model for selecting an optimal access network; constructing a fuzzy hierarchy judgment matrix according to the importance degree relation of every two Qos indexes relative to different services, and obtaining a first weight matrix; constructing an interval hesitant fuzzy multi-attribute matrix according to the interval fuzzy set, and constructing a second weight matrix; combining the first weight matrix and the second weight matrix to generate a final weight matrix; and then according to the interval hesitant fuzzy multi-attribute matrix, calculating the Euclidean distance between the fuzzy scheme and the interval hesitant fuzzy positive ideal scheme and the interval hesitant fuzzy negative ideal scheme, calculating the relative closeness of each scheme to the ideal scheme, and sorting the relative closeness to obtain the optimal access network. According to the invention, the most suitable wireless channel is dynamically selected according to the type of the ship communication service so as to optimize the communication experience of offshore users.

Description

Technical Field

[0001] The present invention relates to the field of marine ship communication technology, and in particular to a ship-side dynamic routing control method based on business and wireless channel dynamic characteristics. Background Art

[0002] In 2018, the International Maritime Organization (IMO) proposed a new concept of Maritime Autonomous Surface Ship (MASS), in which providing safe, reliable and efficient communications is an important challenge for the development of MASS. Therefore, building a low-cost and reliable maritime communication network, improving the effectiveness and reliability of maritime communications, and ensuring the safety of ship navigation have become an important part of the strategy of building a "maritime power". With the increase in the number of ships at sea and the increase in ship-side business needs, traditional communication systems can no longer meet current needs. Complex maritime services have significantly increased the load and channel occupancy of maritime communication links, and aggravated channel congestion. However, maritime communication systems are independent of each other, and there are problems such as inconsistent management and unreasonable bandwidth allocation between systems. In order to achieve the interconnection and interoperability of various heterogeneous networks at sea, heterogeneous network integration has become an urgent need.

[0003] At present, the research at home and abroad is mainly focused on marine heterogeneous networks. This includes the research on ship heterogeneous wireless networks, combining the Internet of Things and ship networking technologies, and systematically studying the switching signals of ship mobile heterogeneous networks. It also includes marine heterogeneous network access selection algorithms based on multi-network parallel transmission. Although these methods provide a certain theoretical basis for the research on marine heterogeneous networks, they do not take into account the data characteristics of the services of ship communication systems, and cannot effectively divide heterogeneous networks. It is difficult to achieve autonomous selection of marine wireless channels, resulting in unreasonable allocation of communication resources and unfavorable unified management of communication equipment. Summary of the invention

[0004] The present invention aims to solve the problems of unreasonable allocation of communication resources and inconsistent management of communication equipment in the process of maritime heterogeneous network integration, and aims to solve their impact on ship navigation safety and effective transmission of communication services. A ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels is proposed. According to the type of ship communication services, the most suitable wireless channel is dynamically selected to optimize the communication experience of maritime users.

[0005] The technical means adopted by the present invention are as follows:

[0006] A ship-side dynamic routing control method based on business and wireless channel dynamic characteristics comprises the following steps:

[0007] S1. Construct a fuzzy hierarchical model for selecting an optimal access network. The fuzzy hierarchical model for selecting an optimal access network includes a target layer, a criterion layer, and a solution layer. The target layer is defined as selecting an optimal access network. The criterion layer is defined as including the following QoS indicators: bandwidth, packet loss rate, jitter, delay, and service price. The solution layer is defined as including the following maritime candidate communication networks: AIS, VDE, Inmarsat, and MF&HF.

[0008] S2. Obtain the ship communication service type and construct a fuzzy hierarchical decision matrix according to the relationship between the importance of the preset pairwise QoS indicators and different services;

[0009] S3, constructing a first weight matrix based on the first decision matrix of the fuzzy hierarchy;

[0010] S4, obtaining a network attribute domain of the candidate communication network, defining an interval fuzzy set on the network attribute domain, and constructing an interval hesitant fuzzy multi-attribute matrix according to the interval fuzzy set;

[0011] S5, constructing a second weight matrix based on the interval hesitant fuzzy multi-attribute matrix;

[0012] S6, combining the first weight matrix and the second weight matrix to generate a final weight matrix;

[0013] S7. Based on the final weight matrix, the interval hesitant fuzzy multi-attribute matrix is ​​weighted, and then the Euclidean distance between the fuzzy solution and the interval hesitant fuzzy positive ideal solution and negative ideal solution is calculated according to the interval hesitant fuzzy multi-attribute matrix. The relative closeness of each solution to the ideal solution is calculated according to the calculated Euclidean distance, and the offshore candidate networks are sorted according to the relative closeness progress to select the optimal access network.

[0014] Furthermore, the interval fuzzy set on the domain is defined in S4 as:

[0015]

[0016] Among them, BW is bandwidth, PLR is packet loss rate, JIT is jitter, DE is delay, SP is service price,

[0017] is the membership interval of the bandwidth belonging to the set H, where is the lower limit of the bandwidth membership interval, is the upper limit of the bandwidth membership interval, is the membership interval whose bandwidth does not belong to the set H, where is the lower limit of the bandwidth membership interval, is the upper limit of the bandwidth membership interval,

[0018] is the membership interval of the packet loss rate belonging to the set H, where is the lower limit of the packet loss rate membership interval, is the upper limit of the packet loss rate membership interval, is the membership interval of the packet loss rate that does not belong to the set H, where is the lower limit of the packet loss rate membership interval, is the upper limit of the packet loss rate membership interval,

[0019] is the membership interval of the jitter belonging to the set H, where is the lower limit of the jitter membership interval, is the upper limit of the jitter membership interval, is the membership interval of the jitter that does not belong to the set H, where is the lower limit of the jitter membership interval, is the upper limit of the jitter membership interval,

[0020] is the membership interval of the delayed set H, where is the lower limit of the delayed membership interval, is the upper limit of the delayed membership interval, is the membership interval of the delay that does not belong to the set H, where is the lower limit of the delayed membership interval, is the upper limit of the delayed membership interval,

[0021] is the membership interval of the service price belonging to the set H, where is the lower limit of the service price membership interval, is the upper limit of the service price membership interval, is the membership interval of the service price that does not belong to the set H, where is the lower limit of the service price membership interval, The upper limit of the service price membership range;

[0022] The interval hesitant fuzzy multi-attribute matrix is:

[0023]

[0024] Among them, m represents the number of service types, and n represents the number of QoS indicators.

[0025] Furthermore, a second weight matrix is ​​constructed based on the interval hesitant fuzzy multi-attribute matrix, including:

[0026] S501, calculating each interval hesitant fuzzy element in the interval hesitant fuzzy multi-attribute matrix based on the interval hesitant fuzzy multi-attribute matrix to obtain a network attribute score, thereby constructing a candidate network attribute score matrix;

[0027] S502, normalizing the elements of the candidate network attribute score matrix;

[0028] S503, constructing a weight matrix of different network parameters in each network based on the candidate network attribute score matrix after element normalization,

[0029] S504: Construct a second weight matrix according to the entropy value corresponding to the weight matrix.

[0030] Furthermore, the candidate network attribute score matrix is:

[0031]

[0032] Among them, m represents the number of business types, n represents the number of QoS indicators, and are the lower and upper limits of the fuzzy interval of the jth attribute of the ith network, and l is the number of intervals.

[0033] Furthermore, the elements of the candidate network attribute score matrix are normalized, including: dividing the Qos index into a benefit attribute index and a cost attribute index, wherein the benefit attribute index includes bandwidth, and the cost attribute index includes packet loss rate, jitter, delay, and service price;

[0034] The normalized formula of benefit attribute index is:

[0035]

[0036] The normalized formula for the cost attribute index is:

[0037]

[0038] Among them, r max Represents the maximum value of the index network attribute score, r min Represents the minimum value of the indicator network attribute score.

[0039] Furthermore, the weight matrix of different network parameters in each network is:

[0040] P=(p ij ) m×n

[0041] in

[0042] Furthermore, the fuzzy hierarchy decision matrix is:

[0043]

[0044] Among them, k represents the type of QoS indicator, and n represents the number of QoS indicators;

[0045] The first weight matrix is:

[0046]

[0047] Among them, m represents the number of service types, and n represents the number of QoS indicators;

[0048] The second weight matrix is:

[0049] W″=(w1″w2″w n ″)

[0050] Among them, e j is the entropy value, n represents the number of QoS indicators, and m represents the number of service types.

[0051] Furthermore, ship communication services include life safety, commercial communications, general communications and ship navigation.

[0052] Furthermore, when constructing the fuzzy hierarchical decision matrix, a 1-9 measurement method is used to refine the level allocation.

[0053] Compared with the prior art, the present invention has the following advantages:

[0054] (1) The ship-side intelligent dynamic routing selection algorithm proposed in the present invention can effectively increase the weight difference between the best and worst offshore candidate networks, thereby quickly identifying the best offshore candidate network channel.

[0055] (2) The present invention selects the ship-side heterogeneous network communication link strictly according to the service requirements and the current network environment, thereby avoiding the network ping-pong effect when switching networks.

[0056] In summary, the present invention ensures the effectiveness of the service selection network and completes the optimal communication link selection of the ship-side heterogeneous network with unified benchmarks, robustness and reliability, which is of great significance to ensuring the safety of ship navigation and effective communication service transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0058] Figure 1 The present invention is a flow chart of a ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels.

[0059] Figure 2 A fuzzy hierarchical model architecture for selecting the optimal access network in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0061] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0062] like Figure 1 As shown, the present invention provides a ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels.

[0063] The ship communication services involved in the present invention include life safety, commercial communication, general communication, and ship navigation; QoS requirements include bandwidth (BW), packet loss ratio (PLR), jitter (JIT), delay (DE), and service price (SP); heterogeneous networks include ship automatic identification system (AIS), very high frequency exchange system (VHF Data Exchange System, VDE), satellite communication system (Inmarsat), medium frequency and high frequency radio bands (MF&HF). The present invention first combines user preferences and determines the first decision weight of network attributes under each service based on the first decision algorithm of ship-side business processing; then obtains the network attribute score matrix according to the interval hesitant fuzzy multi-attribute matrix, thereby obtaining the second weight of the network attribute, which is used to obtain the first weight matrix obtained by the partial positive fuzzy analysis method; finally, the Euclidean distance between any interval fuzzy number and the positive ideal point and the negative ideal point is calculated according to the TOPSIS interval fuzzy multi-attribute decision algorithm, and then the closeness formula of the interval intuitive fuzzy number is given, and the candidate networks are sorted according to the size of the closeness to select the optimal network. The specific steps are as follows:

[0064] S1. Construct a fuzzy hierarchical model for selecting the optimal access network. The fuzzy hierarchical model for selecting the optimal access network includes a target layer, a criterion layer, and a solution layer. Figure 2 As shown in the figure, the target layer is the optimal access network, the criterion layer is the QoS indicators of the candidate maritime communication network, including five indicators: bandwidth, packet loss rate, jitter, delay, and service price; the solution layer is the types of candidate maritime communication networks, including AIS, VDE, Inmarsat, MF&HF.

[0065] S2. Obtain the ship communication service type and construct a fuzzy hierarchical decision matrix according to the relationship between the importance of preset pairwise QoS indicators and different services.

[0066] Specifically, the service decision fuzzy matrix measures the importance of each pair of QoS indicators relative to different services. It uses a 1-9 measurement method to refine the level allocation. The decision indicators of the fuzzy matrix are shown in Table 1. The fuzzy hierarchical decision matrix is:

[0067]

[0068] Where k represents the type of QoS indicator, and n represents the number of QoS indicators.

[0069] Table 1 Fuzzy matrix decision index

[0070]

[0071]

[0072] S3, constructing the first weight matrix based on the first decision matrix of fuzzy hierarchy

[0073] According to the obtained fuzzy hierarchy decision matrix, calculate the first weight of fuzzy hierarchy analysis The first weight is normalized to construct a first weight matrix W′.

[0074] First weight The expression is:

[0075]

[0076] The normalized calculation formula is:

[0077]

[0078] The first weight matrix expression is:

[0079]

[0080] Among them, m represents the number of service types, and n represents the number of QoS indicators.

[0081] S4, obtaining a network attribute domain of the candidate communication network, defining an interval fuzzy set on the network attribute domain, and constructing an interval hesitant fuzzy multi-attribute matrix according to the interval fuzzy set, wherein the definition of the interval fuzzy set is preset according to the network attribute of the communication network.

[0082] Specifically, let X be a given domain, and define the interval fuzzy set H on X as:

[0083]

[0084] in is the hesitation degree of the elements in X to belong to H, and

[0085] The interval hesitant fuzzy set of the present invention is:

[0086]

[0087] Construct interval-hesitant fuzzy multi-attribute matrix G based on interval-hesitant fuzzy sets:

[0088]

[0089] S5, constructing a second weight matrix based on the interval hesitant fuzzy multi-attribute matrix.

[0090] S501. Calculate each interval hesitant fuzzy element in the interval hesitant fuzzy multi-attribute matrix based on the interval hesitant fuzzy multi-attribute matrix to obtain a network attribute score, thereby constructing a candidate network attribute score matrix.

[0091] Depend on Calculate each interval hesitant fuzzy element in the interval hesitant fuzzy multi-attribute matrix to obtain the network attribute score, where, and is the lower and upper limits of the ith interval, and l is the number of intervals. The matrix R is constructed according to the candidate network attribute scores, and the expression is:

[0092]

[0093] Among them, m is the number of candidate networks and n is the number of QoS indicators.

[0094] S502: Normalize the elements of the candidate network attribute score matrix.

[0095] Specifically, the benefit attribute indicates that the larger the index value, the better the effect, while the cost attribute indicates that the smaller the value, the better the effect. Among the five network attributes mentioned in the present invention, except for bandwidth and one benefit attribute, the other four attribute values ​​are cost attributes. In order to facilitate the comprehensive comparison of each network, we normalize the matrix constructed according to the candidate network scores.

[0096] The normalization formula of benefit attribute is:

[0097]

[0098] The cost attribute normalization formula is:

[0099]

[0100] S503: construct a weight matrix for calculating the proportion of different network parameters in each network based on the candidate network attribute score matrix after element normalization.

[0101] After normalizing the decision matrix of the candidate wireless network parameters at sea, the weight matrix of different network parameters in each network is calculated:

[0102] P=(p ij ) m×n

[0103] in

[0104] S504: Construct a second weight matrix according to the entropy value corresponding to the weight matrix.

[0105] The second weight matrix expression is:

[0106] W″=(w″1 w″2 … w″ n )

[0107]

[0108] Among them, e j is the entropy value, n represents the number of QoS indicators, and m represents the number of service types.

[0109] S6. Combining the first weight matrix and the second weight matrix to generate a final weight matrix

[0110] The first service weight calculated by the first decision algorithm for ship-side service processing and the second network weight calculated by the second decision algorithm for ship-side network processing need to be further combined to form the final weight W = (w ij ) m×n , w ij The expression is Where i is the network type, j is the Qos indicator, is the trust of the first weight, is the second weighted trust.

[0111] S7. Based on the final weight matrix, the interval hesitant fuzzy multi-attribute matrix is ​​weighted, and then the Euclidean distance between the fuzzy solution and the interval hesitant fuzzy positive ideal solution and negative ideal solution is calculated according to the interval hesitant fuzzy multi-attribute matrix. The relative closeness of each solution to the ideal solution is calculated according to the calculated Euclidean distance, and the offshore candidate networks are sorted according to the relative closeness progress to select the optimal access network.

[0112] The TOPSIS interval fuzzy multi-attribute decision-making algorithm for ship-side network processing calculates the Euclidean distance between any interval fuzzy number and the positive ideal point and the negative ideal point, and then gives the closeness formula of the interval intuitionistic fuzzy number, and ranks the advantages and disadvantages of the schemes according to the size of the closeness.

[0113] The weighted standard matrix C is constructed by using the first weight obtained by the fuzzy hierarchical analysis method and the second weight obtained by the entropy weight method. ij ) m×n , where c ij =w ij g ij .

[0114] The interval hesitant fuzzy positive ideal solution and negative ideal solution are:

[0115] in

[0116] in

[0117] In the above positive and negative ideal schemes, [μ l ,μ u ] is the membership interval of the set H, where μ l is the lower limit of the membership interval, μ u is the upper limit of the membership interval. l ,v u ] is the membership interval that does not belong to the set H, where v l is the lower limit of the membership interval, v u is the upper limit of the membership interval.

[0118] The expressions of positive and negative ideal solutions of the interval are:

[0119] and

[0120] Any interval fuzzy number c and c + 、c - The Euclidean distance between is:

[0121]

[0122] according to The relative closeness of each solution to the ideal solution is calculated, that is, the degree of closeness to the positive ideal solution and distance from the negative ideal solution. The candidate offshore networks are sorted according to the relative closeness and the optimal access network is selected.

[0123] The scheme and effect of the present invention are further described below through specific application examples:

[0124] The present invention first uses the fuzzy hierarchical analysis method to determine the first weight of the service selection, then uses the entropy method to estimate the second weight of the candidate network, and finally selects the optimal network according to the superior and inferior solution distance interval fuzzy multi-attribute algorithm. The specific steps are as follows:

[0125] 1. Determine the fuzzy hierarchical model

[0126] Fuzzy hierarchical model Figure 2 As shown in the figure, the target layer is the optimal access network, the criterion layer is the QoS indicators of the candidate maritime communication network, including five indicators: bandwidth, packet loss rate, jitter, delay, and service price; the solution layer is the types of candidate maritime communication networks, including AIS, VDE, Inmarsat, MF&HF.

[0127] 2. Construct the fuzzy hierarchical first decision matrix

[0128] Constructing a judgment matrix is ​​to compare each element with each other and determine the weight of each criterion layer to the target layer. Simply put, it is to judge the indicators of the criterion layer in pairs. The constructed judgment matrix is ​​as follows:

[0129] The first fuzzy decision matrix for life safety:

[0130]

[0131] The first fuzzy decision matrix of ship navigation:

[0132]

[0133] The first fuzzy decision matrix for commercial communications:

[0134]

[0135] The first fuzzy decision matrix for general communication:

[0136]

[0137] 3. Business weight calculation

[0138] According to the obtained fuzzy hierarchy decision matrix, the first weight of fuzzy hierarchy analysis is calculated, and the first weight is normalized to construct the first weight matrix. The weights of the four service types are as follows:

[0139]

[0140]

[0141] 4. Constructing interval hesitant fuzzy multi-attribute matrix

[0142] The interval-valued hesitant fuzzy set is:

[0143]

[0144] According to the interval-hesitant fuzzy set, the interval-hesitant fuzzy multi-attribute matrix is ​​constructed as follows:

[0145]

[0146] 5. Calculate network attribute scores

[0147] Depend on Calculate each interval hesitant fuzzy element in the interval hesitant fuzzy multi-attribute matrix to obtain the network attribute score, where, and is the lower and upper bounds of the ith interval, and l is the number of intervals. Construct the matrix R 0 based on the candidate network attribute scores:

[0148]

[0149] Among them, m is the number of candidate networks and n is the number of QoS indicators.

[0150] 6. Matrix Normalization

[0151] The benefit attribute indicates that the larger the index value, the better the effect, while the cost attribute indicates that the smaller the value, the better the effect. Among the five network attributes mentioned in the present invention, except for bandwidth and one benefit attribute, the other four attribute values ​​are cost attributes. In order to facilitate the comprehensive comparison of each network, we normalize the matrix constructed according to the candidate network scores.

[0152] The normalization formula of benefit attribute is:

[0153]

[0154] The cost attribute normalization formula is:

[0155]

[0156] The normalized matrix is

[0157]

[0158] 7. Calculate the proportion of network parameters

[0159] After normalizing the decision matrix of the candidate wireless network parameters at sea, the weight matrix P of different network parameters in each network is calculated. ij ) m×n ,in The weight matrix of different network parameters is:

[0160] 8. Calculate the final weight

[0161] The second weight matrix expression is: where e j is the corresponding entropy value,

[0162] The calculated second weight is w″ j =(0.367 0.367 0.098 0.054 0.114)

[0163] 9. TOPSIS interval fuzzy multi-attribute decision making algorithm

[0164] The TOPSIS interval fuzzy multi-attribute decision-making algorithm for ship-side network processing calculates the Euclidean distance between any interval fuzzy number and the positive ideal point and the negative ideal point, and then gives the closeness formula of the interval intuitive fuzzy number. The schemes are ranked according to the closeness. The network selection results of the life safety business obtained by the TOPSIS interval fuzzy multi-attribute decision-making algorithm are as follows:

[0165]

[0166] According to the above selection results, it can be concluded that the optimal network selected by the ship-side network processing TOPSIS interval fuzzy multi-attribute decision-making algorithm meets the QoS requirements of life safety services.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels, characterized in that: The following steps are involved: S1. Construct a fuzzy hierarchical model for selecting an optimal access network. The fuzzy hierarchical model for selecting an optimal access network includes a target layer, a criterion layer, and a solution layer. The target layer is defined as selecting an optimal access network. The criterion layer is defined as including the following QoS indicators: bandwidth, packet loss rate, jitter, delay, and service price. The solution layer is defined as including the following maritime candidate communication networks: AIS, VDE, Inmarsat, and MF&HF. S2. Obtain the ship communication service type and construct a fuzzy hierarchical decision matrix according to the relationship between the importance of the preset pairwise QoS indicators and different services; S3, constructing a first weight matrix based on the first decision matrix of the fuzzy hierarchy; S4, obtaining a network attribute domain of the candidate communication network, defining an interval fuzzy set on the network attribute domain, and constructing an interval hesitant fuzzy multi-attribute matrix according to the interval fuzzy set; S5, constructing a second weight matrix based on the interval hesitant fuzzy multi-attribute matrix; S6, combining the first weight matrix and the second weight matrix to generate a final weight matrix; S7. Based on the final weight matrix, the interval hesitant fuzzy multi-attribute matrix is ​​weighted, and then the Euclidean distance between the fuzzy solution and the interval hesitant fuzzy positive ideal solution and negative ideal solution is calculated according to the interval hesitant fuzzy multi-attribute matrix. The relative closeness of each solution to the ideal solution is calculated according to the calculated Euclidean distance, and the offshore candidate networks are sorted according to the relative closeness progress to select the optimal access network.

2. A ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels according to claim 1, characterized in that: The interval fuzzy set on the domain is defined in S4 as: Among them, BW is bandwidth, PLR is packet loss rate, JIT is jitter, DE is delay, SP is service price, is the membership interval of the bandwidth belonging to the set H, where is the lower limit of the bandwidth membership interval, is the upper limit of the bandwidth membership interval, is the membership interval whose bandwidth does not belong to the set H, where is the lower limit of the bandwidth membership interval, is the upper limit of the bandwidth membership interval, is the membership interval of the packet loss rate belonging to the set H, where is the lower limit of the packet loss rate membership interval, is the upper limit of the packet loss rate membership interval, is the membership interval of the packet loss rate that does not belong to the set H, where is the lower limit of the packet loss rate membership interval, is the upper limit of the packet loss rate membership interval, is the membership interval of the jitter belonging to the set H, where is the lower limit of the jitter membership interval, is the upper limit of the jitter membership interval, is the membership interval of the jitter that does not belong to the set H, where is the lower limit of the jitter membership interval, is the upper limit of the jitter membership interval, is the membership interval of the delayed set H, where is the lower limit of the delayed membership interval, is the upper limit of the delayed membership interval, is the membership interval of the delay that does not belong to the set H, where is the lower limit of the delayed membership interval, is the upper limit of the delayed membership interval, is the membership interval of the service price belonging to the set H, where is the lower limit of the service price membership interval, is the upper limit of the service price membership interval, is the membership interval of the service price that does not belong to the set H, where is the lower limit of the service price membership interval, The upper limit of the service price membership range; The interval hesitant fuzzy multi-attribute matrix is: Among them, m represents the number of service types, and n represents the number of QoS indicators.

3. The ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels according to claim 1 is characterized in that: Constructing a second weight matrix based on the interval hesitant fuzzy multi-attribute matrix includes: S501, calculating each interval hesitant fuzzy element in the interval hesitant fuzzy multi-attribute matrix based on the interval hesitant fuzzy multi-attribute matrix to obtain a network attribute score, thereby constructing a candidate network attribute score matrix; S502, normalizing the elements of the candidate network attribute score matrix; S503, constructing a weight matrix of different network parameters in each network based on the candidate network attribute score matrix after element normalization, S504: Construct a second weight matrix according to the entropy value corresponding to the weight matrix.

4. A ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels according to claim 3, characterized in that: The candidate network attribute score matrix is: Among them, m represents the number of business types, n represents the number of QoS indicators, and are the lower and upper limits of the fuzzy interval of the jth attribute of the ith network, and l is the number of intervals.

5. A ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels according to claim 4, characterized in that: Normalizing the elements of the candidate network attribute score matrix, including: dividing the Qos index into a benefit attribute index and a cost attribute index, wherein the benefit attribute index includes bandwidth, and the cost attribute index includes packet loss rate, jitter, delay, and service price; The normalized formula of benefit attribute index is: The normalized formula for the cost attribute index is: Among them, r max Represents the maximum value of the index network attribute score, r min Represents the minimum value of the indicator network attribute score.

6. A ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels according to claim 5, characterized in that: The weight matrix of different network parameters in each network is: P=(p ij ) m×n in 7. A ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels according to claim 6, characterized in that: The fuzzy hierarchy decision matrix is: Among them, k represents the type of QoS indicator, and n represents the number of QoS indicators; The first weight matrix is: Among them, m represents the number of service types, and n represents the number of QoS indicators; The second weight matrix is: W″=(w1″w2″w n ″) Among them, e j is the entropy value, n represents the number of QoS indicators, and m represents the number of service types.

8. The ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels according to claim 1 is characterized in that: Ship communication services include life safety, commercial communications, general communications and ship navigation.

9. The ship-side dynamic routing control method based on the dynamic characteristics of services and wireless channels according to claim 1 is characterized in that: When constructing the fuzzy hierarchical decision matrix, a 1-9 measurement method is used to refine the level allocation.

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