High-dynamic TSN path optimization method and device based on connectivity probability

Abstract

The invention provides a high-dynamic TSN path optimization method and device based on a communication probability. The method comprises the following steps: S1, predicting a physical space position of a communication node in real time; s2, calculating a link connection probability, a path connection probability and a path network connection probability according to the prediction result; s3, quantitatively evaluating the network resource state according to the path network connection probability and the quantified demand constraint condition to obtain an evaluation result; and S4, constructing an end-to-end transmission path network by adopting a dynamic optimal path mechanism based on an evaluation result. According to the method, a dynamic optimal path mechanism is provided on the basis of real-time prediction of the physical space position of each communication node, dynamic construction and quantitative evaluation are performed on the path and the path network on the basis of connectivity, the method has perspectiveness and high real-time performance in time, the network topology with high dynamic change can be well matched, and the service quality is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of network communications, and in particular to a highly dynamic TSN path optimization method and device based on connection probability. Background Art

[0002] With the opening and development of low-altitude airspace, multi-UAV collaboration is increasingly used in wilderness search and rescue, forestry monitoring, emergency rescue and other fields. With the continuous improvement of UAV maneuverability and the expansion of application scenarios, the communication network topology for UAVs presents the characteristics of high dynamic changes, the types of services carried are more abundant, and the requirements for real-time and reliability are getting higher and higher, which has typical TSN characteristics.

[0003] In typical wireless ad hoc networks and TSNs, the transmission quality of services is mainly guaranteed by comprehensive application of service priority classification, traffic scheduling, path management and other mechanisms. These mechanisms mainly perform a priori planning based on statistical analysis of historical data in combination with application scenarios, and allocate network resources such as storage, bandwidth, and paths in a passive manner. They have achieved good results in application scenarios with relatively stable network topologies. However, in application scenarios with drastic changes in network topology, they face a series of challenges such as frequent link reconstruction, frequent reconstruction of network topology, and frequent data retransmission, making it difficult to guarantee service transmission quality.

[0004] The present invention aims to predict the link connectivity probability by deeply analyzing the movement characteristics of communication nodes in combination with the movement position probability model, construct an end-to-end connectivity probability calculation method, and propose a path optimization mechanism on this basis to improve the service transmission quality under high dynamic conditions from the perspective of path management. Summary of the invention

[0005] In view of the defects in the prior art, the object of the present invention is to provide a highly dynamic TSN path optimization method and device based on connectivity probability.

[0006] A highly dynamic TSN path optimization method based on connectivity probability provided by the present invention includes:

[0007] Step S1: Real-time prediction of the physical spatial position of the communication node;

[0008] Step S2: Calculate the link connectivity probability, path connectivity probability and path network connectivity probability according to the prediction results;

[0009] Step S3: quantitatively evaluate the network resource status according to the path network connection probability and the quantified demand constraints to obtain an evaluation result;

[0010] Step S4: Based on the evaluation results, a dynamic optimal path mechanism is used to construct an end-to-end transmission path network.

[0011] Preferably, it also includes:

[0012] Step S5: The path selection process is verified by simulation tests and physical experiments in turn, and the verification results are output.

[0013] Preferably, step S1 includes analyzing the position, velocity and acceleration of the communication node at the current moment, and calculating the position probability density distribution at the next moment.

[0014] Preferably, the calculation process of the link connectivity probability includes:

[0015] The wireless transmission model is used to analyze the relationship between the transmission signal quality and distance between communication nodes, and the probability density distribution of the single node position is combined for calculation.

[0016] Preferably, the calculation process of the path network connection probability includes:

[0017] The node probability of each node is calculated in turn based on a recursive method. When it converges, the node probability represents the path network connection probability between the two nodes.

[0018] Preferably, the dynamic optimal path mechanism can select multiple paths between source and destination nodes to construct a dynamic path network, replacing a single optimal transmission path in the form of a path network.

[0019] Preferably, the process of the dynamic optimal path mechanism includes:

[0020] Determine the initial optimal path based on the routing protocol and link shielding conditions;

[0021] Obtain the transmission delay of the path and evaluate whether it meets business requirements;

[0022] Calculate the path network connectivity probability. If the calculation result does not meet the business requirements, put the link with the lowest connectivity probability into the link shielding pool.

[0023] Repeat the above steps until a path network that meets the requirements is obtained.

[0024] Preferably, the quantitative demand constraints include the end-to-end transmission delay, end-to-end connectivity probability and the number of occupied network resources required by the service; the dynamic optimal path mechanism guides the transmission of the service by comprehensively evaluating the end-to-end connectivity situation.

[0025] A highly dynamic TSN path optimization device based on connectivity probability provided by the present invention includes:

[0026] Communication payload module;

[0027] The communication load module includes a communication processing unit, a channel unit and an omnidirectional antenna;

[0028] The communication processing unit includes a communication processing board, which is used to implement a dynamic optimal path mechanism and undertake channel access and control, network management and control, and business transmission processing; the communication processing board is connected to the channel unit and the omnidirectional antenna to complete wireless communication with other communication nodes.

[0029] Preferably, the communication processing unit also includes an audio and video information collection unit, an infrared information collection unit and a meteorological information collection unit, which are used to collect different types of data.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The present invention proposes a dynamic optimal path mechanism based on the real-time prediction of the physical space position of each communication node itself, and dynamically constructs paths and path networks based on connectivity. It is forward-looking and highly real-time in time, and can better match highly dynamic network topology changes.

[0032] 2. The dynamic path optimization mechanism proposed in the present invention is based on the location distribution probability of each communication node, the link connectivity probability, the path connectivity probability and the path optimization. Compared with the traditional evaluation method based on routing detection, which has the difficulty in evaluating the link status and giving the evaluation results of "on and off" and "good or bad" for the path status, the present invention innovatively adopts a probabilistic method to perform quantitative and refined evaluation on the status of these network resources, making the evaluation results more accurate.

[0033] 3. The dynamic optimal path mechanism proposed in the present invention can not only proactively allocate relevant network resources with "clear purpose" and "clear constraints" based on the dynamic prediction and quantitative evaluation of the status of network resources such as links and paths in accordance with specific needs, but also ensure the stability of the network topology to ensure the quality of service transmission by more proactive means such as "clearly purposeful" adjustment of node positions or adding a "clear number" of nodes, thereby purposefully shaping the network topology in accordance with specific needs.

[0034] 4. In the dynamic optimal path mechanism proposed in the present invention, an end-to-end transmission path network is constructed by adopting a dynamic optimal path mechanism based on quantitative evaluation of link and path status combined with quantitative demand constraints; from the perspective of network performance, this mechanism can not only respond to drastic changes in network topology in a timely manner to maintain the stability of transmission performance, but also make full use of network resources; from the perspective of human-computer interaction, it can not only quantitatively transmit user demands for network transmission performance, network resource management and control, etc. to the optimal mechanism, but also point out the bottleneck links of network performance and the weak aspects of network resource configuration through the optimal mechanism, so as to promote users to timely adjust the configuration of network equipment and adjust the transmission service type and service volume.

[0035] Other beneficial effects of the present invention will be explained in the specific implementation manner through the introduction of specific technical features and technical solutions. Through the introduction of these technical features and technical solutions, those skilled in the art should be able to understand the beneficial technical effects brought about by the technical features and technical solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0037] Figure 1 It is a directed graph based on network topology in the present invention.

[0038] Figure 2 It is a schematic diagram of the motion state analysis of the communication node in the present invention.

[0039] Figure 3 This is a flow chart of recursive calculation of path network connectivity probability in the present invention.

[0040] Figure 4 This is a flow chart of the path optimization mechanism in the present invention.

[0041] Figure 5 This is a network topology state diagram at the initial moment in an embodiment of the present invention.

[0042] Figure 6 This is a network topology state diagram at the 15th second in an embodiment of the present invention.

[0043] Figure 7 This is a network topology state diagram at the 17th second in an embodiment of the present invention.

[0044] Figure 8 This is a network topology state diagram at the end time in an embodiment of the present invention.

[0045] Fig. 9 Schematic diagram of the cumulative packet loss rate between source and destination nodes in an embodiment of the present invention.

[0046] Fig.10 Schematic diagram of the number of nodes occupied during transmission between source and destination nodes in an embodiment of the present invention.

[0047] Fig.11 It is a connection probability diagram between source and destination nodes in the preferred mechanism in the embodiment of the present invention.

[0048] Fig.12 Graph showing the relationship between packet loss rate and node movement speed in an embodiment of the present invention.

[0049] Fig.13 This is a block diagram of the test platform in the present invention.

[0050] Fig.14This is a principle block diagram of the communication processing board in the present invention.

[0051] Fig.15 This is a physical picture of the communication processing board in the present invention.

[0052] Fig.16 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0053] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0054] Reference Fig.16 As shown, a highly dynamic TSN path optimization method based on connectivity probability includes:

[0055] Step S1: Real-time prediction of the physical spatial position of the communication node; analyzing the position, velocity and acceleration of the communication node at the current moment, and calculating the position probability density distribution at the next moment.

[0056] Step S2: Calculate the link connectivity probability, path connectivity probability and path network connectivity probability based on the prediction results:

[0057] The logarithmic attenuation model is used to analyze the relationship between the quality of the transmission signal and the distance between communication nodes, and the link connection probability is calculated according to the distance-probability attenuation function.

[0058] The node probability of each node is calculated in turn based on a recursive method. When it converges, the node probability represents the path network connection probability between the two nodes.

[0059] Step S3: quantitatively evaluate the network resource status according to the path network connection probability and the quantified demand constraints to obtain an evaluation result;

[0060] Step S4: Based on the evaluation results, a dynamic optimal path mechanism is used to construct an end-to-end transmission path network.

[0061] Quantitative demand constraints include the end-to-end transmission delay, end-to-end connectivity probability, and the amount of network resources required by the service. The dynamic optimal path mechanism guides the transmission of services by comprehensively evaluating the end-to-end connectivity.

[0062] The dynamic optimal path mechanism can select multiple paths between the source and destination nodes to build a dynamic path network, replacing a single optimal transmission path with a path network. The process includes:

[0063] Determine the initial optimal path based on the routing protocol and link shielding conditions;

[0064] Obtain the transmission delay of the path and evaluate whether it meets business requirements;

[0065] Calculate the path network connectivity probability. If the calculation result does not meet the business requirements, put the link with the lowest connectivity probability into the link shielding pool.

[0066] Repeat the above steps until a path network that meets the requirements is obtained.

[0067] Step S5: The path selection process is verified by simulation tests and physical experiments in turn, and the verification results are output.

[0068] The present invention also provides a highly dynamic TSN path optimization device based on connection probability, comprising:

[0069] Communication payload module;

[0070] The communication payload module includes a communication processing unit, a channel unit and an omnidirectional antenna;

[0071] The communication processing unit contains a communication processing card, which is used to implement the dynamic optimal path mechanism and undertake channel access and control, network management and control, and business transmission processing. The communication processing card is connected to the channel unit and the omnidirectional antenna to complete wireless communication with other communication nodes.

[0072] The communication processing unit also includes sub-units such as an audio and video information collection unit, an infrared information collection unit and a meteorological information collection unit, which are used to collect different types of data.

[0073] The mechanism proposed in the present invention is to dynamically construct paths and path networks based on connectivity, based on real-time prediction of the physical spatial position of each communication node. It is forward-looking and highly real-time in time, and can better match highly dynamic network topology changes. TSN and other technologies use network topologies built based on periodic routing detection for path selection and allocation, which has obvious lag in time. Under highly dynamic conditions, there will be obvious mapping errors between the inter-node link relationship in the network topology and the actual inter-node link status.

[0074] The mechanism proposed in this invention is based on the location distribution probability of each communication node, the link connection probability, the path connection probability and the path optimization, and innovatively adopts a probabilistic approach to quantitatively and finely evaluate the status of these network resources. This evaluation method is different from the routing detection evaluation method adopted by TSN, etc. The evaluation method based on routing detection is difficult to evaluate the link status, and usually only gives the evaluation results of "on and off" and "good or bad" for the path status. These evaluation results are relatively rough and can generally only be used for qualitative analysis.

[0075] The above is a basic embodiment of the present invention. The technical solution of the present invention is further described below through a preferred embodiment.

[0076] Example 1

[0077] This embodiment proposes a highly dynamic TSN path optimization method based on connectivity probability, and the working principle is as follows:

[0078] 1. Network topology analysis

[0079] In a wireless communication network composed of multiple UAVs, the improvement of the mobility of communication nodes causes frequent changes in link and path status, which in turn leads to rapid changes in network topology, significantly affecting the transmission performance of the network.

[0080] like Figure 1 As shown in the figure, at a certain moment, the communication load U1 of the UAV is used as the source node, U n As the destination node, a network topology graph from the source end to the destination end is formed; for the convenience of analysis, the network topology graph is described as a two-dimensional directed graph G = (V, E) without loss of generality, where V is a set of n nodes, E is a set of all edges, and each edge can be regarded as a link, and its direction is consistent with the direction of information transmission in the link.

[0081] At two adjacent nodes U x , U y The link U x-y It can be regarded as an edge of a directed graph, and its weight can be determined by the link connection probability P x-y Indicates. At nodes U1, U n Based on a series of links, "U1→U2→…→U n ”…“U1→U3→…→U n ", etc. Based on these paths, a path network can be constructed for information transmission. The connectivity between two nodes can be described as the connectivity probability P of the path network. 路径网 .

[0082] 2. Calculation of the connectivity probability of the path network

[0083] In highly dynamic application scenarios, communication nodes usually carry sensors that sense their own motion status in real time. By analyzing their current position, speed, and acceleration, they can calculate the probability density distribution of their position at the next moment, and then obtain the connectivity probability of the link, calculate the connectivity probability of the path, and the path network.

[0084] like Figure 2 As shown, for the convenience of analysis, it is assumed that the communication node U x , Uy The maximum speed value is v max The maximum acceleration value is a max ; At a certain moment, they are at points A and B, and their velocity vectors are AA′ and BB′ respectively; The possible positions they can reach per unit time are H x , H y .

[0085] With communication node U x For example, within the unit time Δt, the position that can be reached is in a circle with point A as the center, R1 = v max The velocity change is in the circle A with radius Δt; the velocity change is in the circle with point A′ as the center, R2=a max Δt is the radius of the circle A′; the current speed can be recorded as v x =|AA′|, the area where circle A and circle A′ overlap is denoted as J x After unit time Δt, according to the overlap between circle A and circle A′, U can be calculated in two cases: x The probability density distribution of the position f(H x ).

[0086] (1) If the circle A′ is completely inside the circle A, then H x It may be located inside or on circle A′, that is, for a max +v x ≤v max ,have:

[0087]

[0088] (2) If the circle A′ is partially inside the circle A, due to v max The circle A represents the maximum position range that the node can reach. At this time, H x Can only be located in area J x Or the arc L located on circle A intercepted by circle A′ x On, that is, for a max +v x >v max , calculate area J x Area:

[0089] when When:

[0090]

[0091] when When:

[0092]

[0093] in:

[0094]

[0095] In area J x Inside, node U x Position H x The probability density is uniformly distributed, and the probability density function is:

[0096]

[0097] For circle A', the area J x and arc L x The part outside the node is limited by the maximum position range of the node and can only move to arc L at most in a unit time. x So the arc L x The position probability density function on needs to be added with this part, and the probability density function of the additional part is:

[0098]

[0099] Where θ is the value located on arc L x Position H x A′H to A′ x The angle between AA′ and AA′.

[0100] In summary, the position J of the node after unit time Δt is x The probability density function of is:

[0101] When H x Located in Area J x Inside, there are:

[0102]

[0103] When H x Located in arc L x On, there are:

[0104]

[0105] In order to facilitate the analysis, the logarithmic attenuation model is used to analyze the relationship between the quality and distance of the transmission signal between communication nodes. x-y The connectivity of link U can be described as a distance-probability decay function D(d), where d is the distance between two nodes. x-y The connection probability is:

[0106]

[0107] In actual experiments, due to the calculation of P 链路 The analytical solution is relatively complex, and the present invention adopts the Monte Carlo method to solve it through computer programming.

[0108] The directed graph G is built on the basis of the network topology graph. Its structure does not contain loop feedback structure. Through disassembly and merging, it can be equivalent to a combination of a series of serial structures and parallel structures based on the set E. That is, the connection probability of the path network can be expressed as:

[0109] P 路径网 =g(P 串联 ,P 并联 )

[0110] in, It represents the connectivity probability of a single path formed by connecting two nodes head to tail through m links in sequence. represents the connection probability of the path network formed by m paths between two nodes. n The network structure formed between them is generally complex, and it is difficult to directly decompose the series and parallel connections. Here, we use computer programming to solve the connection probability between two nodes. According to the forward transmission direction of each edge of the directed graph G, the nodes in the set V form an upstream and downstream relationship. For the convenience of discussion, the node U j The node probability is denoted as P j , P j Represents nodes U1, U j The connection probability between j The downstream node is denoted as U k , the corresponding node probability is recorded as P k ; The node probability of the source node U1 is 1. According to the direction and probability weight of each edge in G, starting from U1, the node probability of each node is calculated recursively. After convergence, U n The node probability of nodes U1, U n The connection probability P between 路径网 The specific calculation process is as follows Figure 3 shown.

[0111] 3. Path optimization mechanism

[0112] In communication networks, path discovery is usually performed based on a routing protocol, and then an optimal path is selected between the source and destination nodes for information transmission. Within a routing cycle, the transmission performance of this optimal path is usually considered to remain unchanged and optimal. Combined with the routing information, its end-to-end transmission delay can be determined.

[0113] Under high dynamic conditions, the transmission delay and connectivity performance of this optimal path may deteriorate rapidly over time. It is necessary to conduct dynamic evaluation by combining the quantitative evaluation information of the link and path and relevant calculation methods. On this basis, through the path selection mechanism, multiple paths are screened between the source and destination nodes to construct a dynamic path network, replacing the single optimal transmission path in the form of a path network to meet the reliability and real-time requirements of the service for end-to-end transmission. For the convenience of discussion, these requirements are described here as the connectivity probability P0 and path delay t0 of the path network. Then, the following relationship should be satisfied among the link, path, path network, and service requirements: P 路径网 ≥P0, t 路径 ≤t0,

[0114] As Figure 4 shown, the path selection mechanism uses the end-to-end transmission delay, end-to-end connectivity probability, and the amount of network resources occupied by the service as constraints, generates and examines each path, dynamically constructs a path network, and then comprehensively evaluates the end-to-end connectivity to guide the transmission of the service. The main steps of the path selection mechanism process are as follows:

[0115] 1) Determine an optimal path W1 between the source and destination nodes according to the routing protocol;

[0116] 2) Obtain the transmission delay t1 of the optimal path W1 according to the routing information;

[0117] 3) According to the service's requirement for transmission delay, that is, t 路径 ≤t0, judge the situation of the transmission delay t1 of the optimal path W1;

[0118] 4) When t1≤t0, calculate the connectivity probability P1 of the optimal path W1 according to the P 串联 formula;

[0119] 5) When t1>t0, the optimal path W1 does not meet the service's transmission delay requirement, and it is necessary to return to step "1)" to re-determine the optimal path;

[0120] 6) When P1≥P0, the optimal path W1 can be regarded as a path network that meets the requirements, that is, P 路径网 =P1≥P0, and the service will be transmitted on the optimal path W1;

[0121] 7) When P1<P0, the connectivity probability of the optimal path W1 is not sufficient to meet the service's transmission requirements. Put W1 into the path network, and then calculate the connectivity probability P 并联 of the path network at this time according to the P 路径网 , then, P 路径网 =P1<P0;

[0122] 8) Analyze the connectivity probabilities of all links constituting the path network, and put the link with the smallest connectivity probability into the link shielding pool;

[0123] 9) Return to step "1)" in the process, after shielding all links in the link shielding pool, determine the new optimal path between the source and destination nodes according to the routing protocol, and then proceed to step "2)" to obtain its transmission delay, and then proceed to step "3)" to determine its transmission delay. Figure 4 The process is carried out in an orderly manner until a path network that meets the requirements is obtained.

[0124] In actual applications, due to severe degradation of the network topology during certain periods of time, it may be impossible to obtain a path network that meets the requirements according to the above process, and users need to actively adjust transmission requirements or suspend the transmission of high-reliability services. In addition, in the above process, by introducing the link of the path network occupying network resource status, the path optimization mechanism is further enriched, allowing users to have a more comprehensive grasp of the network transmission performance.

[0125] 4. Simulate the above method

[0126] Based on NS3 network simulation software, a simulation framework suitable for multi-UAV application scenarios is built, and the NS2MobilityHelper class is used to preset the initial position and motion trajectory of each communication node; the transmission channel adopts a logarithmic attenuation model, and sets the reference distance to 50 units and the attenuation logarithm to 10. Under the same network environment, standard TSN mechanisms such as 802.1CB and 802.1Qca are selected as comparison objects, and GPSR (Greedy Parameter StatelessRouting) is selected as the routing protocol in the path optimization mechanism. Simulation analysis is carried out from aspects such as packet loss rate, network resource occupancy rate, and high dynamic adaptability.

[0127] like Figure 5 The figure shows the network topology when the communication network starts simulating; Figure 6 , Figure 7 This is the network topology after each node has moved for 15 seconds and 17 seconds respectively; Figure 8 This is the network topology at the end of the simulation. The communication network consists of 10 nodes, each with a preset maximum speed of 25 and a maximum acceleration of 7. The source node is 10.0.0.1, the destination node is 10.0.0.10, and the connection probability between the source and destination nodes is preset to 0.7. When the connection probability between nodes is greater than or equal to 0.7, the graph is connected by a solid line, and when it is less than 0.7 and higher than 0.3, it is connected by a dotted line. A lower connection probability is no longer shown in the graph. 3 seconds after the simulation is started, data is sent and received between the source and destination nodes.

[0128] like Fig. 9The figure shows the packet loss rate of data transmitted between source and destination nodes under the same simulation conditions when using the typical TSN mechanism and the path optimization mechanism. Among them, the cumulative packet loss rate of the path optimization mechanism is reduced by more than 10%; Fig.10 It shows the number of nodes occupied by the two mechanisms during the transmission process. Among them, the number of nodes occupied by the path optimization mechanism is reduced by more than 40% on average; Fig.11 It represents the connection probability between the source and destination nodes during the transmission process when the path optimization mechanism is adopted. Among them, node 10.0.0.8 moves to the farthest position where it can communicate with nodes 10.0.0.7 and 10.0.0.9 at the 15th second, and is completely disconnected from the entire communication network at the 17th second.

[0129] In order to further investigate the relationship between node movement speed and service transmission quality, the number of nodes in the simulation environment is adjusted to 20, the preset maximum acceleration of each node is 7, the connection probability between the source and destination nodes is preset to 0.9, and the packet loss rate is used to compare the service transmission quality of nodes at different movement speeds. Fig.12 shown.

[0130] This embodiment also provides a highly dynamic TSN path optimization device based on connectivity probability, and uses a test platform to test and verify the above method.

[0131] The test platform is composed of a commercial drone, sensors, channel components and self-developed communication processing boards. Fig.13 As shown in the figure, the communication processing board is mainly responsible for channel access and control, network management and control, business transmission processing and other functions. Fig.14 , Fig.15 shown.

[0132] In the actual test, due to the limitations of the test site and the maneuverability of the UAV, 6 test platforms were used in the test, and the effective communication distance and motion performance of each node were matched accordingly. After starting, these UAVs were remotely controlled by the ground station and flew along the pre-planned path. After the flight, the data of each node was recovered. After analyzing the test data, the conclusions of the physical experiment were consistent with those of the simulation experiment, that is, the end-to-end transmission reliability, real-time performance, and network resource utilization based on the path optimization mechanism are better.

[0133] The mechanism proposed in the present invention can, based on the dynamic prediction and quantitative evaluation of the status of network resources such as links and paths, actively allocate relevant network resources with "clear purposes" and "clear constraints" in combination with specific needs. It can also ensure the stability of the network topology by adjusting the node positions with "clear purposes" or adding "clear numbers" of nodes in a more active way to ensure the quality of service transmission, and thus purposefully shape the network topology in combination with specific needs. This is different from the passive allocation of network resources such as TSN. In the passive allocation method, since the network resource status is obtained passively, the qualitative evaluation results are inaccurate and not very objective, and it is difficult to perform a refined decomposition of specific needs and drive specific network resources to achieve corresponding goals in a purposeful and constrained manner. Network resources are usually allocated based on experience and data statistical results, and the resource utilization rate is low and the pertinence is not strong. There is also a lack of the ability to actively schedule and allocate network resources to reshape the network topology and continuously ensure the quality of service transmission.

[0134] In the mechanism proposed by the present invention, a dynamic optimal path mechanism is used to construct an end-to-end transmission path network based on quantitative evaluation of link and path status combined with quantitative demand constraints. From the perspective of network performance, this mechanism can not only respond to drastic changes in network topology in a timely manner to maintain stable transmission performance, but also make full use of network resources; from the perspective of human-computer interaction, it can not only quantitatively transmit user requirements for network transmission performance, network resource management and control to the optimal mechanism, but also point out the bottleneck links of network performance and the weak aspects of network resource configuration through the optimal mechanism, so as to promote users to adjust the configuration of network equipment, the type of services and the amount of services transmitted in a timely manner. TSN and other commonly used path control and reservation mechanisms allocate path resources. The path control method is usually based on route detection, and the optimal path selected by the route is selected. The optimal path is usually the path with the shortest transmission time in the network topology based on route detection. In order to solve the bandwidth, stability and other problems faced by the optimal path, multiple suboptimal paths are reserved in advance by combining experience or data statistics to replace the optimal path or enhance the performance of the optimal path. This path control and reservation mechanism causes a large amount of network resources to be idle in order to ensure transmission performance. Under highly dynamic conditions, this passive path control mechanism is difficult to adapt to drastic changes in topology, and it is difficult to guarantee transmission performance even when network resources are sufficient.

[0135] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0136] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A highly dynamic TSN path optimization method based on connectivity probability, characterized in that: include: Step S1: Real-time prediction of the physical spatial position of the communication node; Step S2: Calculate the link connectivity probability, path connectivity probability and path network connectivity probability according to the prediction results; Step S3: quantitatively evaluate the network resource status according to the path network connection probability and the quantified demand constraints to obtain an evaluation result; Step S4: Based on the evaluation results, a dynamic optimal path mechanism is used to construct an end-to-end transmission path network.

2. The high-dynamic TSN path optimization method based on connectivity probability according to claim 1 is characterized in that: Also includes: Step S5: The path selection process is verified by simulation tests and physical experiments in turn, and the verification results are output.

3. The high-dynamic TSN path optimization method based on connectivity probability according to claim 1 is characterized in that: The step S1 includes analyzing the position, speed and acceleration of the communication node at the current moment, and calculating the position probability density distribution at the next moment.

4. The high-dynamic TSN path optimization method based on connectivity probability according to claim 1 is characterized in that: The calculation process of link connectivity probability includes: The wireless transmission model is used to analyze the relationship between the transmission signal quality and distance between communication nodes, and the probability density distribution of the single node position is combined for calculation.

5. The high-dynamic TSN path optimization method based on connectivity probability according to claim 1 is characterized in that: The calculation process of the path network connectivity probability includes: The node probability of each node is calculated in turn based on a recursive method. When it converges, the node probability represents the path network connection probability between the two nodes.

6. The high-dynamic TSN path optimization method based on connectivity probability according to claim 1 is characterized in that: The dynamic optimal path mechanism can select multiple paths between source and destination nodes to build a dynamic path network, replacing a single optimal transmission path in the form of a path network.

7. The high-dynamic TSN path optimization method based on connectivity probability according to claim 6 is characterized in that: The process of the dynamic optimal path mechanism includes: Determine the initial optimal path based on the routing protocol and link shielding conditions; Obtain the transmission delay of the path and evaluate whether it meets business requirements; Calculate the path network connectivity probability. If the calculation result does not meet the business requirements, put the link with the lowest connectivity probability into the link shielding pool. Repeat the above steps until a path network that meets the requirements is obtained.

8. The high-dynamic TSN path optimization method based on connectivity probability according to claim 7 is characterized in that: The quantitative demand constraints include the end-to-end transmission delay, end-to-end connectivity probability and the number of network resources occupied by the service. The dynamic optimal path mechanism guides the transmission of the service by comprehensively evaluating the end-to-end connectivity.

9. A highly dynamic TSN path optimization device based on connectivity probability, using the highly dynamic TSN path optimization method based on connectivity probability as described in any one of claims 1 to 8, characterized in that: include: Communication payload module; The communication load module includes a communication processing unit, a channel unit and an omnidirectional antenna; The communication processing unit includes a communication processing board, which is used to implement a dynamic optimal path mechanism and undertake channel access and control, network management and control, and business transmission processing; the communication processing board is connected to the channel unit and the omnidirectional antenna to complete wireless communication with other communication nodes.

10. The highly dynamic TSN path optimization device based on connectivity probability according to claim 9, characterized in that: The communication processing unit also includes an audio and video information collection unit, an infrared information collection unit and a meteorological information collection unit, which are used to collect different types of data.

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