Probability analysis method and optimization scheduling method for guaranteeing 5G-TSN network transmission reliability

Abstract

The invention discloses a probability analysis method and an optimal scheduling method for guaranteeing the transmission reliability of a 5G-TSN (5G-Transport Sensor Network), which are used for carrying out probabilistic modeling on the 5G-TSN and emphatically describing the probability characteristics of 5G data injected into the TSN so as to describe the time slot occupation condition of the TSN in an expected form and provide a novel 5G-TSN modeling mode. The reliability of the 5G-TSN is analyzed in detail through indexes such as SINR and packet loss rate, and the end-to-end transmission reliability of the 5G-TSN is improved by adopting a non-feedback retransmission K-time repetition mode. In 5G uplink access, a signal-to-noise ratio, a packet loss rate, a modulation and coding technology (MCS) and a reliability threshold are adopted to jointly establish a retransmission frequency calculation expression, so that the transmission condition of a 5G-TSN network is analyzed more accurately, and resource reservation is carried out.

Description

Technical Field

[0001] The present invention mainly relates to the field of communication technology, and in particular to a probability analysis method and an optimization scheduling method for ensuring the reliability of 5G-TSN network transmission. Background Art

[0002] With the rise of latency-sensitive services, traditional industrial networks are evolving towards deterministic networks. Time Sensitive Networking (TSN) has become an important support solution for achieving deterministic transmission in industrial sites due to its series of fine-grained traffic scheduling protocols. In order to further break free from wired constraints and enable ubiquitous perception and flexible manufacturing, a 5G and TSN converged network architecture has been proposed, which is expected to usher in a new era of industrial communication networks. However, the harsh production environment of factories has seriously affected the reliability of 5G air interface transmission, exacerbating the fusion bottleneck of "difficulty in determining performance matching" in 5G-TSN industrial networks. Therefore, how to improve the transmission performance of 5G networks and build a seamlessly converted and deeply integrated 5G-TSN network has become the key to enabling industrial scenarios.

[0003] As one of the key technologies for improving the reliability of 5G networks, retransmission technology can alleviate packet loss by sending multiple copies and improve the determinism of air interface transmission. There are currently two mainstream retransmission technologies: feedback-based retransmission and feedback-free retransmission. Among them, feedback-based retransmission schemes, such as Hybrid Automatic RepeatreQuest (HARQ), must wait for the reception of feedback signals before retransmission operations, which greatly increases the reliability of the 5G air interface at the cost of greatly increasing transmission delay. On the other hand, the 5G URLLC scenario introduces feedback-free retransmission schemes, such as K repetitions, which actively send multiple consecutive copies without waiting for feedback. Compared with feedback-based retransmission, the transmission delay of the feedback-free retransmission scheme will be significantly reduced, and it is increasingly suitable for delay-sensitive scenarios. However, due to the lack of feedback signals, the time when the base station successfully decodes the data packet is unknown, which brings uncertainty when 5G data is injected into the TSN network. Therefore, how to characterize the uncertainty of the injection time of the TSN network under feedback-free retransmission of the 5G network and establish a reliability-guaranteed 5G-TSN end-to-end transmission model is crucial to achieve deterministic transmission of delay-sensitive services.

[0004] As an important mathematical tool, probability is widely used to measure and deal with uncertainty. Based on the probabilistic framework, we can make reasonable inferences and decisions in the face of incomplete information. Based on this, we assume that 5G data has probabilistic characteristics when injected into TSN, and consider the uncertainty brought by 5G retransmission in a probabilistic form, so that the data transmission and load distribution of the TSN network can be inferred, and then an important basis for 5G-TSN network resource allocation can be provided. In the worst case, 5G data must wait for all copies to be transmitted before it can be injected into the TSN network, which will result in a higher queuing delay. Through probabilistic modeling, 5G data can achieve wait-free forwarding from the base station to the TSN source node, greatly reducing the end-to-end transmission delay, and better meeting the needs of industrial scenarios for low-latency and high-reliability transmission.

[0005] After searching the existing literature, it is found that the most similar implementation scheme is Chinese patent application number: 202110718076.8, named: 5G and TSN joint scheduling method based on wireless channel information, and its specific scheme is: designing the retransmission factor through the wireless channel information, and establishing the transmission delay budget of the 5G system based on the retransmission factor to eliminate the transmission time jitter of the data packet in the 5G system. Although this method also uses a retransmission scheme to deal with the impact of wireless channel instability on end-to-end transmission determinism, the 5G transmission delay budget it constructs is an analysis of the worst case, which is significantly different from the research focus of this invention. The patent application number is: 202210649601.X, named: A 5G-TSN industrial heterogeneous virtual network architecture and virtual resource fine-grained scheduling method, and its specific scheme is: using SDN and NFV technologies to virtualize and model 5G-TSN, introducing the age of information (AoI) to characterize the real-time nature of 5G access, and based on this, dynamically adjusting the priority of TSN injection to ensure end-to-end deterministic delivery. The scheme focuses on end-to-end delay, and the design environment is too idealized, without considering the packet loss that may occur in 5G access. The patent application number is: 202311251389.2, and the name is: 5G and TSN collaborative flow scheduling system and method for industrial Internet. The specific scheme is: design the CG resources of the stream client in GF-NOMA technology, and jointly optimize the 5G transmission CG time-frequency resources and the time slot offset injected into the TSN network through deep learning to achieve seamless transmission between the 5G network and the TSN network. The scheme adopts a deterministic forwarding strategy when setting the process from base station to TSN gateway, that is, the data will arrive at the TSN network deterministically, which is significantly different from the purpose of solving the injection uncertainty caused by the unknown moment of base station decoding success in the present invention. The patent application number is: 202311748248.1, and the name is: Probabilistic repetitive coding access method for uplink sporadic loads. The specific scheme is: when users with sporadic loads access the 5G network, a competitive access strategy based on probabilistic continuous repetitive coding is adopted to maximize transmission reliability by jointly optimizing the activation probability and the number of retransmissions. However, this solution only addresses the 5G network access problem and has not considered the 5G-TSN converged network application scenario. Based on the above analysis, the current research environment for 5G-TSN is too idealistic, and the analysis of transmission reliability in network modeling is almost blank, and there is no patent related to the probabilistic modeling of 5G-TSN networks. Therefore, analyzing the transmission reliability and model of 5G-TSN networks in a probabilistic form is a very innovative research idea. Summary of the invention

[0006] In view of the above-mentioned defects of the prior art, the technical problems to be solved by the present invention include:

[0007] How to design a probabilistic analysis method to ensure the reliability of 5G-TSN network transmission, and how to design an optimized scheduling method based on the above probabilistic analysis method.

[0008] To achieve the above object, the present invention provides a probability analysis method for ensuring the reliability of 5G-TSN network transmission, which is characterized by comprising the steps of:

[0009] Step 1, calculate the number of retransmissions k by using the signal-to-noise ratio, MCS, packet loss rate and reliability threshold;

[0010] Step 2: Allocate k retransmissions in the 5G network and establish a 5G load balancing indicator;

[0011] Step 3, calculating the transmission success rate of 5G data in the allocated time slot;

[0012] Step 4: Calculate the forwarding time slot and forwarding probability after the 5G data is injected into the TSN network;

[0013] Step 5: Describe the TSN network load balancing indicators in the desired form;

[0014] Step 6: Establish the 5G-TSN network load balancing optimization problem.

[0015] Furthermore, step 1 is specifically as follows:

[0016] Calculate the signal-to-noise ratio γ of user n n , select an MCSm to derive the packet loss rate P under this channel environment m (γ n ), and calculates the 5G time-frequency domain resources s required to transmit one copy based on the selected MCS n ;

[0017] The reliability threshold θ is obtained based on the user's QoS requirements and the packet loss rate P m The calculation expression of the number of retransmissions k is established by using the signal-to-noise ratio γ

[0018] Furthermore, step 2 is specifically as follows:

[0019] Convert the packet deadline to the maximum slot offset that the 5G network can allocate n , in time slot 1~o n The k retransmissions calculated in step 2 are allocated above, where the number of retransmissions of user n in the i-th time slot is expressed as

[0020] At the same time, corresponding 5G transmission resources, namely time-frequency domain resource blocks (RBs), need to be allocated for k retransmissions. Used to indicate whether RB(i,j) is allocated to user n;

[0021] Therefore, the load balancing indicator in the 5G system is defined as the bandwidth occupancy rate of each time slot ρ i , where J represents the total number of RBs that can be allocated on the time slot, that is:

[0022]

[0023] Furthermore, step 3 is specifically as follows:

[0024] Construct the 5G data transmission success rate of user n in time slot i for:

[0025]

[0026] Furthermore, in step 4,

[0027] The forwarding probability of the 5G data when injected into the TSN network is calculated as follows:

[0028] By selecting the 5G parameter set and configuring the mini-time slot, the time slot lengths of 5G and TSN are set to be consistent, both d;

[0029] In the 5G network, after a data packet completes the transmission of a time slot, it will be immediately injected into the TSN network. User n receives the data from the i-th 5G time slot. The probability of injecting into the tth TSN time slot is Where t = i + 1;

[0030] In the TSN network, data will be transmitted with this probability;

[0031] The calculation method of the forwarding time slot after the 5G data is injected into TSN is:

[0032] In TSN network, the forwarding characteristics of CQF model can be used to Calculate the flow f n Forwarding slot on link ε

[0033] Construct the representation flow f n A Boolean variable indicating whether the link ε is passed in time slot t It is in the following form, where r n Represents the flow f n Cycle:

[0034]

[0035] Furthermore, step 5 is specifically as follows:

[0036] By packet size l n , forwarding probability and existential variables Construct the following expected load expression, that is, the slot occupancy of the link ε at time slot t for:

[0037]

[0038] Furthermore, step 6 is specifically as follows:

[0039] The TSN load balancing indicator is defined as the time slot occupancy of the link. TSN gateways usually have the same queue capacity Λ. Calculate the time slot occupancy of the TSN network on the time slot t and link ε for:

[0040]

[0041] A multi-objective optimization problem for 5G-TSN network load balancing is constructed to minimize ρ i and To improve the schedulability of the network.

[0042]

[0043] An optimization scheduling method based on the aforementioned probability analysis method for ensuring 5G-TSN network transmission reliability includes:

[0044] Phase 1, initialization scheme;

[0045] Stage 2, the results between groups were combined;

[0046] Stage 3, dynamic adjustment within the group;

[0047] Phase 4, judgment termination.

[0048] Furthermore, the phase 1 is specifically as follows: traffic with the same period and transmission path is divided into a group, and 5G RBs transmission resources and TSN queue resources are allocated according to the period attribute, data packet size attribute and retransmission number attribute of the traffic set in each group; at the same time, a priority is set for each traffic in the group. Then greedy,incremental scheduling is performed according to the priority, thus obtaining the initial,scheduling plan for each group;

[0049] The stage 2 specifically includes: combining the groups of initial scheduling solutions generated in the stage 1 to form a global scheduling solution; in this process, a taboo search method is used to find the optimal solution when the groups of scheduling solutions are combined.

[0050] Furthermore, the stage 3 is specifically as follows: based on the merged result of stage 2, find the group with the TSN load peak value and record the current TSN load balancing index value of the group In this stage, this indicator is used as a new constraint and added to the scheduling framework of stage 1 for rescheduling. That is, when the traffic in the group is greedily and incrementally scheduled according to priority, it is necessary to additionally check whether the load balancing indicators of each time slot of the TSN network in the group are less than

[0051] The stage 4 is specifically as follows: the termination condition is defined as the change in the 5G-TSN load balancing indicator no longer shows a downward trend, that is, Among them, ω1 and ω2 are weight factors used to balance the magnitude of 5G and TSN load balancing indicators; if the termination condition is met, the scheduling ends and the final scheduling plan is formed; if not, it returns to stage 2 and continues to iterate stage 2 and stage 3.

[0052] Compared with the prior art solutions, the technical effects of the present invention are:

[0053] The present invention performs probabilistic modeling on the 5G-TSN network, focusing on the probabilistic characteristics of 5G data injection into TSN, thereby taking an expected form to describe the time slot occupancy of the TSN network, providing a novel 5G-TSN modeling method, which well describes the coupling relationship between 5G and TSN in the case of no feedback retransmission, and more reasonably analyzes and infers the load distribution of the 5G-TSN network.

[0054] The present invention analyzes the reliability of the 5G-TSN network in detail through indicators such as SINR and packet loss rate, and adopts a method of retransmission without feedback K times to improve the end-to-end transmission reliability of the 5G-TSN network. Transmission is carried out under the condition of reliability guarantee, which greatly improves the network performance and is more robust to deal with interference caused by complex industrial environments.

[0055] In the 5G uplink access, the present invention uses the signal-to-noise ratio, packet loss rate, modulation and coding technology (MCS) and reliability threshold to jointly establish the retransmission number calculation expression, which is used to more accurately analyze the transmission situation of the 5G-TSN network and reserve resources. A more scientific setting of the retransmission number makes the delay calculation and load analysis of the 5G-TSN network more accurate, more in line with the fine-grained resource reservation characteristics of TSN, and minimizes the waste of transmission delay while ensuring the deterministic delivery of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the 5G-TSN industrial heterogeneous network of the present invention;

[0057] Figure 2is a flow chart of the probability analysis method of the present invention;

[0058] Figure 3 It is a flow chart of the optimization scheduling method of the present invention. DETAILED DESCRIPTION

[0059] The following describes several preferred embodiments of the present invention with reference to the drawings in the specification, so that the technical content is clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0060] The present invention aims to solve the problem of uncertainty in the time when 5G data is injected into TSN when the feedback-free retransmission technology is applied in the 5G-TSN network to ensure the end-to-end transmission reliability. To this end, the present invention proposes a probability analysis method to effectively characterize the uncertainty caused by 5G retransmission, and further proposes an optimization scheduling method based on the probability analysis results to improve the schedulability of the 5G-TSN network. This method will be Figure 1 The 5G-TSN industrial heterogeneous network shown in the figure provides a complete scheduling solution. The process of the probability analysis method is as follows Figure 2 As shown in the figure, the flow chart of the scheduling scheme based on probability analysis is shown in Figure 3 .

[0061] This embodiment adopts Figure 1 In the 5G-TSN industrial heterogeneous network architecture shown in the figure, the underlying device data is uploaded through the 5G access network, and the TSN network is responsible for data forwarding as the core network. The 5G system uses a K-time repetition scheme as a reliability guarantee mechanism, that is, the user device performs a fixed K transmission attempts for each data packet. At the same time, TSN is configured using the Cyclic Quening Forwarding (CQF) model proposed in the IEEE 802.1Qch protocol.

[0062] like Figure 2 As shown, a probability analysis method for ensuring the reliability of 5G-TSN network transmission is characterized by: comprising the steps;

[0063] Step 1, calculate the number of retransmissions k by using the signal-to-noise ratio, MCS, packet loss rate and reliability threshold;

[0064] Step 2: Allocate k retransmissions in the 5G network and establish a 5G load balancing indicator;

[0065] Step 3, calculating the transmission success rate of 5G data in the allocated time slot;

[0066] Step 4: Calculate the forwarding time slot and forwarding probability after the 5G data is injected into the TSN network;

[0067] Step 5: Describe the TSN network load balancing indicators in the desired form;

[0068] Step 6: Establish the 5G-TSN network load balancing optimization problem.

[0069] Step 1 is as follows:

[0070] Calculate the signal-to-noise ratio γ of user n n , select an MCSm to derive the packet loss rate P under this channel environment m (γ n ), and calculates the 5G time-frequency domain resources s required to transmit one copy based on the selected MCS n ;

[0071] The reliability threshold θ is obtained based on the user's QoS requirements and the packet loss rate P m The calculation expression of the number of retransmissions k is established by using the signal-to-noise ratio γ

[0072] Step 2 is as follows:

[0073] Convert the packet deadline to the maximum slot offset that the 5G network can allocate. n , in time slot 1~o n The k retransmissions calculated in step 2 are allocated above, where the number of retransmissions of user n in the i-th time slot is expressed as

[0074] At the same time, corresponding 5G transmission resources, namely time-frequency domain resource blocks (RBs), need to be allocated for k retransmissions. Used to indicate whether RB(i,j) is allocated to user n;

[0075] Therefore, the load balancing indicator in the 5G system is defined as the bandwidth occupancy rate of each time slot ρ i , where J represents the total number of RBs that can be allocated on the time slot, that is:

[0076]

[0077] Step 3 is as follows:

[0078] If at least one of the k copies is correctly decoded, the data transmission in the 5G network is considered successful. Assume that the 5G base station has a filtering function, that is, if multiple data packets are successfully decoded in the same time slot, the base station only allows one data packet to be injected into the TSN. Based on the above two points, the 5G data transmission success rate of user n in time slot i is constructed for:

[0079]

[0080] Furthermore, in step 4,

[0081] The forwarding probability of the 5G data when injected into the TSN network is calculated as follows:

[0082] By selecting the 5G parameter set and configuring the mini-time slot, the time slot lengths of 5G and TSN are set to be consistent, both d;

[0083] In the 5G network, after a data packet completes the transmission of a time slot, it will be immediately injected into the TSN network. User n receives the data from the i-th 5G time slot. The probability of injecting into the tth TSN time slot is Where t = i + 1;

[0084] In the TSN network, data will be transmitted with this probability;

[0085] The calculation method of the forwarding time slot after the 5G data is injected into the TSN network is:

[0086] In TSN network, the forwarding characteristics of CQF model can be used to Calculate the flow f n Forwarding slot on link ε

[0087] Construct the representation flow f n A Boolean variable indicating whether the link ε is passed in time slot t It is in the following form, where r n Represents the flow f n Cycle:

[0088]

[0089] Step 5 is as follows:

[0090] Due to the lack of feedback signals, the time slot when the 5G data packet is successfully decoded is unknown, which will lead to randomness in the TSN load distribution. For this problem, we use Chebyshev's law of large numbers to infer and describe the TSN network load. n , forwarding probability and existential variables Construct the following expected load expression, that is, the slot occupancy of the link ε at time slot t for:

[0091]

[0092] Step 6 is as follows:

[0093] The TSN load balancing indicator is defined as the time slot occupancy of the link. TSN gateways usually have the same queue capacity Λ. Calculate the time slot occupancy of the TSN network on the time slot t and link ε for:

[0094]

[0095] Since the field data is uploaded through the 5G network and forwarded by the TSN network, and the time and probability of 5G data injection into the TSN network have been constructed in steps 3 and 4, we can unify the planning of the load distribution of the 5G-TSN network through the resource allocation and retransmission allocation of the 5G network, so as to simplify the complexity of heterogeneous network collaborative scheduling. According to the 5G and TSN load balancing indicators shown in steps 2 and 6 respectively, we constructed a multi-objective optimization problem for 5G-TSN network load balancing, aiming to minimize ρ i and To improve the schedulability of the network.

[0096]

[0097] In order to solve the above optimization problem, an optimization scheduling method is proposed based on the aforementioned probability analysis method, including:

[0098] Phase 1, initialization scheme;

[0099] Stage 2, the results between groups were combined;

[0100] Stage 3, dynamic adjustment within the group;

[0101] Phase 4, judgment termination.

[0102] The specific steps of the first stage are: dividing the traffic with the same period and transmission path into a group, and allocating 5G RBs transmission resources and TSN queue resources according to the period attribute, data packet size attribute and retransmission number attribute of the traffic set in each group; at the same time, setting the priority for each traffic in the group Then greedy,incremental scheduling is performed according to the priority, thus obtaining the initial,scheduling plan for each group;

[0103] The stage 2 specifically includes: combining the groups of initial scheduling solutions generated in the stage 1 to form a global scheduling solution; in this process, a taboo search method is used to find the optimal solution when the groups of scheduling solutions are combined.

[0104] The specific steps of stage 3 are as follows: based on the merged results of stage 2, find the group with the TSN load peak value and record the current TSN load balancing index value of the group. In this stage, this indicator is used as a new constraint and added to the scheduling framework of stage 1 for rescheduling. That is, when the traffic in the group is greedily and incrementally scheduled according to priority, it is necessary to additionally check whether the load balancing indicators of each time slot of the TSN network in the group are less than

[0105] The stage 4 is specifically as follows: the termination condition is defined as the change in the 5G-TSN load balancing indicator no longer shows a downward trend, that is, Among them, ω1 and ω2 are weight factors used to balance the magnitude of 5G and TSN load balancing indicators; if the termination condition is met, the scheduling ends and the final scheduling plan is formed; if not, it returns to stage 2 and continues to iterate stage 2 and stage 3.

[0106] The present invention is significantly different from previous studies that only focus on end-to-end delay optimization of 5G-TSN networks. It deeply considers the interference and impact of the actual factory environment on the 5G channel, and takes reliability as the research focus of the 5G-TSN network. It predicts and reduces the loss rate of data packets in harsh environments through probability analysis, thereby providing more accurate and reliable solutions in practical applications and improving the adaptability and robustness of the network. Applying probability theory to the transmission situation analysis of 5G-TSN networks is an innovative method that can more accurately predict and deal with uncertainties in the network, so as to better enable the 5G-TSN network to the industrial site. In addition, the present invention can dynamically adjust the data transmission strategy according to the changes in the current network environment, including adjusting the transmission timing of data packets, the allocation of the number of retransmissions, etc., so as to effectively reduce data packet loss, while improving transmission reliability while maintaining low latency requirements as much as possible, and meet the strict requirements of industrial scenarios for real-time performance. Through refined probability analysis and scheduling, the present invention can optimize the allocation of 5G-TSN network resources, such as bandwidth, time slots, etc., to avoid unnecessary waste of resources. While ensuring transmission reliability, the overall efficiency and throughput of the network are improved, providing a more powerful network support for industrial Internet of Things applications.

[0107] In complex industrial environments, through the application of this invention, it is expected that the packet loss rate in the 5G-TSN network can be significantly reduced, achieving 99.9-99.999% transmission reliability, ensuring the complete transmission of critical data. While ensuring high reliability, the end-to-end delay can still be effectively controlled to meet the real-time requirements of industrial applications, and the average end-to-end transmission delay range is controlled within milliseconds. The 5G-TSN network load distribution is quantified through a probabilistic model, and resource idleness and waste are reduced through a reasonable scheduling method, thereby improving the overall network performance.

[0108] With the rapid development of Industry 4.0 and intelligent manufacturing, 5G-TSN network is the key infrastructure of the future network, and its transmission reliability is directly related to the production efficiency and product quality of the factory. The probability analysis and scheduling method of the present invention provides strong technical support for the widespread application of 5G-TSN network in the industrial field by solving the bottleneck problem of transmission reliability in the actual environment. It is expected that this method will be widely used in many fields such as intelligent manufacturing, automated production lines, remote monitoring and maintenance, etc., to promote the digital transformation of industry and enhance the competitiveness of the industry.

[0109] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The description and examples are to be considered exemplary only, and the true scope and spirit of the present invention is indicated by the following claims.

[0110] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A probabilistic analysis method for ensuring 5G-TSN network transmission reliability, characterized by: Includes steps: Step 1, calculate the number of retransmissions k by using the signal-to-noise ratio, MCS, packet loss rate and reliability threshold; Step 2: Allocate k retransmissions in the 5G network and establish a 5G load balancing indicator; Step 3, calculating the transmission success rate of 5G data in the allocated time slot; Step 4, calculate the forwarding time slot and forwarding probability after 5G data is injected into TSN; Step 5: Describe the TSN network load balancing indicators in the desired form; Step 6: Establish the 5G-TSN network load balancing optimization problem.

2. According to the probability analysis method for ensuring 5G-TSN network transmission reliability according to claim 1, the step 1 is specifically: Calculate the signal-to-noise ratio γ of user n n , select an MCSm to derive the packet loss rate P under this channel environment m (γ n ), and calculates the 5G time-frequency domain resources s required to transmit one copy based on the selected MCS n ; The reliability threshold θ is obtained based on the user's QoS requirements and the packet loss rate P m The calculation expression of the number of retransmissions k is established by using the signal-to-noise ratio γ 3. The probability analysis method for ensuring 5G-TSN network transmission reliability according to claim 2 is characterized in that: The step 2 is specifically as follows: Convert the packet deadline to the maximum slot offset that the 5G network can allocate. n , in time slot 1~o n k calculated in step 2 i The number of retransmissions of user n in the i-th time slot is expressed as At the same time, corresponding 5G transmission resources, namely time-frequency domain resource blocks (RBs), need to be allocated for k retransmissions. Used to indicate whether RB(i,j) is allocated to user n; Therefore, the load balancing indicator in the 5G system is defined as the bandwidth occupancy rate of each time slot ρ i , where J represents the total number of RBs that can be allocated on the time slot, that is:

4. The probability analysis method for ensuring 5G-TSN network transmission reliability according to claim 3 is characterized in that: The step 3 is specifically as follows: Construct the 5G data transmission success rate of user n in time slot i for:

5. The probability analysis method for ensuring 5G-TSN network transmission reliability according to claim 4 is characterized in that: In step 4, The forwarding probability of the 5G data when injected into the TSN network is calculated as follows: By selecting the 5G parameter set and configuring the mini-time slot, the time slot lengths of 5G and TSN are set to be consistent, both d; In the 5G network, after a data packet completes the transmission of a time slot, it will be immediately injected into the TSN network. User n receives the data from the i-th 5G time slot. The probability of injecting into the tth TSN time slot is Where t = i + 1; In the TSN network, data will be transmitted with this probability; The calculation method of the forwarding time slot after the 5G data is injected into the TSN network is: In TSN network, the forwarding characteristics of CQF model can be used to Calculate the flow f n Forwarding slot on link ε Construct the representation flow f n A Boolean variable indicating whether the link ε is passed in time slot t It is in the following form, where r n Represents the flow f n Cycle:

6. The probability analysis method for ensuring 5G-TSN network transmission reliability according to claim 5 is characterized in that: The step 5 is specifically as follows: By packet size l n , forwarding probability and existential variables Construct the following expected load expression, that is, the slot occupancy of the link ε at time slot t for:

7. The probability analysis method for ensuring 5G-TSN network transmission reliability according to claim 6 is characterized in that: The step 6 is specifically as follows: The TSN load balancing indicator is defined as the time slot occupancy of the link. TSN gateways usually have the same queue capacity Λ. Calculate the time slot occupancy of the TSN network on the time slot t and link ε for: A multi-objective optimization problem for 5G-TSN network load balancing is constructed to minimize ρ i and To improve the schedulability of the network, 8. An optimization scheduling method based on the probability analysis method for ensuring 5G-TSN network transmission reliability according to claims 1-7, characterized in that: include: Phase 1, initialization scheme; Stage 2, the results between groups were combined; Stage 3, dynamic adjustment within the group; Phase 4, judgment termination.

9. The optimization scheduling method according to claim 9, characterized in that: The specific steps of the first stage are: dividing the traffic with the same period and transmission path into a group, and allocating 5G RBs transmission resources and TSN queue resources according to the period attribute, data packet size attribute and retransmission number attribute of the traffic set in each group; at the same time, setting the priority for each traffic in the group Then greedy,incremental scheduling is performed according to the priority, thus obtaining the initial,scheduling plan for each group; The stage 2 specifically includes: combining the groups of initial scheduling solutions generated in the stage 1 to form a global scheduling solution; in this process, a taboo search method is used to find the optimal solution when the groups of scheduling solutions are combined.

10. The optimization scheduling method according to claim 9, characterized in that: The specific steps of stage 3 are as follows: based on the merged results of stage 2, find the group with the TSN load peak value and record the current TSN load balancing index value of the group. In this stage, this indicator is used as a new constraint and added to the scheduling framework of stage 1 for rescheduling. That is, when the traffic in the group is greedily and incrementally scheduled according to priority, it is necessary to additionally check whether the load balancing indicators of each time slot of the TSN network in the group are less than The stage 4 is specifically as follows: the termination condition is defined as the change in the 5G-TSN load balancing indicator no longer shows a downward trend, that is, Where ω1 and ω2 are weight factors used to balance 5G and TSN The magnitude of the load balancing indicator; if the termination condition is met, the scheduling ends and the final scheduling plan is formed; If not satisfied, return to stage 2 and continue to iterate and execute stages 2 and 3.

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