CN112511462A - Software-defined industrial heterogeneous time-sensitive network system and resource scheduling method - Google Patents

Software-defined industrial heterogeneous time-sensitive network system and resource scheduling method Download PDF

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CN112511462A
CN112511462A CN202011496917.7A CN202011496917A CN112511462A CN 112511462 A CN112511462 A CN 112511462A CN 202011496917 A CN202011496917 A CN 202011496917A CN 112511462 A CN112511462 A CN 112511462A
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tsn
data
time
flow
industrial
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CN112511462B (en
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许齐敏
张雅静
陈彩莲
关新平
李明妍
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Shanghai Jiaotong University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/50—Queue scheduling
    • H04L47/62—Queue scheduling characterised by scheduling criteria
    • H04L47/625—Queue scheduling characterised by scheduling criteria for service slots or service orders
    • H04L47/6275—Queue scheduling characterised by scheduling criteria for service slots or service orders based on priority
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
    • H04L47/2425—Traffic characterised by specific attributes, e.g. priority or QoS for supporting services specification, e.g. SLA
    • H04L47/2433—Allocation of priorities to traffic types
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/50—Queue scheduling
    • H04L47/56—Queue scheduling implementing delay-aware scheduling
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/50—Queue scheduling
    • H04L47/62—Queue scheduling characterised by scheduling criteria
    • H04L47/6295—Queue scheduling characterised by scheduling criteria using multiple queues, one for each individual QoS, connection, flow or priority
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/24—Negotiating SLA [Service Level Agreement]; Negotiating QoS [Quality of Service]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/56—Allocation or scheduling criteria for wireless resources based on priority criteria
    • H04W72/566—Allocation or scheduling criteria for wireless resources based on priority criteria of the information or information source or recipient
    • H04W72/569—Allocation or scheduling criteria for wireless resources based on priority criteria of the information or information source or recipient of the traffic information

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Abstract

The invention discloses a software-defined industrial time-sensitive heterogeneous network system and a resource scheduling method, and relates to the field of industrial communication. A Software-Defined industrial heterogeneous Time Sensitive network architecture comprises an industrial field layer, an SDTSN (Software Defined Time Sensitive Networking) transmission layer and an industrial cloud platform. A software-defined industrial heterogeneous time-sensitive network resource hierarchical scheduling method comprises a multi-priority wireless scheduling mechanism considering burst data based on a 5G technology and a wired TSN multi-priority queue mapping and scheduling mechanism. The problems that an existing industrial communication mode is single, QoS requirements of industrial field mass data are different and the like are solved, data streams with different QoS requirements can be dispatched in a centralized mode based on global information of a network, the on-demand deterministic delivery of industrial heterogeneous data is achieved, the full-process time sensitive communication of a wireless-wired industrial heterogeneous network is achieved, and the time certainty and the resource utilization rate of a TSN (traffic service network) are greatly improved.

Description

Software-defined industrial heterogeneous time-sensitive network system and resource scheduling method
Technical Field
The invention relates to the field of industrial communication, in particular to a software-defined industrial heterogeneous time-sensitive network system and a resource scheduling method.
Background
The arrival of industry 4.0 represented by technologies such as cloud computing, industrial internet of things and big data, which mainly adopts intelligent manufacturing, marks that the industrial production mode is changed from traditional automatic production to intelligent production, and the industrial production demand is changed from traditional large-batch large-scale production to small-batch customized flexible production. Therefore, in a novel industrial production mode, how to transmit bottom layer mass sensing data in a suitable and reliable manner according to production requirements, a real-time network with deterministic bounded network delay and jitter is established, and the integration of sensing, sensing and controlling is realized to become a major bottleneck which is a major breakthrough in flexible production. However, the industrial field is distributed over different buses of a plurality of automatic equipment manufacturers, and all bus protocols are not communicated with each other, so that the fusion and utilization of bottom layer data are greatly influenced. For this reason, in 2016, the IEEE 802.1 working group proposed a Time Sensitive Network protocol (TSN) to provide a uniform transport frame and deterministic transport service for various heterogeneous data.
As one of TSN protocols, the PSFP (Per-stream Filtering and Policing) mechanism proposed in IEEE 802.1Qci (data stream Filtering and Policing) protocol indicates that data has different priorities when being injected into different queues of an input port of a TSN gateway, and can be matched with a Time Aware gate (TAS) mechanism proposed in IEEE 802.1Qbv to realize multi-priority deterministic transmission of data inside the TSN network. However, currently, there is no research on the queue injection mechanism of the TSN gateway, and there is no clear model for the mapping relationship between the queue injection mechanism and the transmission delay.
At present, the TSN protocol is only suitable for a wired transmission scene, the wireless TSN protocol is still in a starting stage, the industrial field environment is complex, the coexistence of a large number of buses causes the difficulty of equipment maintenance, one type of process industry represented by a hot rolling production line has a relatively severe production environment, many areas needing to measure sensing data often have buses which are difficult to reach, the measurement in a wired mode cannot be adopted, the measurement of perfecting field data needs to be matched with a wireless sensor, and the maintenance cost can be greatly improved by deploying wired communication in some broad industrial monitoring environments, therefore, the communication demand of novel industry cannot be met only by the wired TSN protocol.
In addition, although the TSN technology can realize low-delay and high-reliability delivery of data through a series of mechanisms, each TSN switch is often only responsible for local multi-queue data stream scheduling, and if a TSN gated list design of a whole network is to be realized, centralized network topology information must be possessed and timely forwarding scheduling must be performed. The fast forwarding function provided by a Software Defined Network (SDN) is well suited to such a scenario that the topology is adjusted in a centralized and fast manner to meet the demand delivery of multi-QoS data.
In addition, the sensing data of the industrial site has different QoS (Quality of Service) requirements, for example, the slowly-changing temperature data has lower priority, the image data for monitoring the surface Quality of the roller has higher priority, and the like, and how to flexibly schedule and deliver the image data according to different transmission requirements of the data is one of the requirements of industrial 4.0 intelligent manufacturing. For example, the patent numbers are: 201810541014.2, the name is: a TSN scheduling method facing real-Time application requirements provides a Time Sensitive Software Defined Network (TSSDN) framework based on a Time Sensitive Network (TSN) and a Software Defined Network (SDN) technology, utilizes the characteristic that an SDN controller can flexibly change Network topology through logic Programming, comprehensively considers the Time delay constraint of each data flow, maximizes the energy utilization rate in a Network based on Integer Linear Programming (ILP), but because the SDN controller is a centralized controller, the SDN controller needs to make decision issuing based on the global information of the Network, and industrial field Time generates massive heterogeneous data, the SDN controller cannot guarantee the decision real-Time performance for the dynamic change of the massive field data, and only aims at the global configuration scheduling problem of a wired TSN protocol, and the problems of wired/wireless heterogeneous and wireless data stream scheduling and the like are not involved. Therefore, how to design a scheduling mechanism of multi-priority data facing an industrial field is still a very challenging problem.
Besides the frequently occurring multi-QoS perception data in the industrial field, the dynamically sporadic real-time information is also a very important safety critical information, which often contains alarm information related to production safety, such as: the emergency braking information, the fault information and the like have the characteristics of aperiodic generation, high real-time requirement and the like, and the satisfaction of the transmission requirement of the information is also a big premise and challenge for ensuring the safety of industrial production.
The search of the existing literature finds that the most similar implementation scheme is the Chinese patent application number: 201710712382.4, the name is: a cloud computing network architecture and a method for enhancing the reliability of the cloud computing network architecture are disclosed, which specifically comprise the following steps: a three-tier network architecture is proposed comprising: infrastructure layer, fog calculation layer and cloud service layer, the fog calculation layer comprises a plurality of fog calculation node, and every fog calculation node manages an infrastructure area, for example: the sensor nodes, the intelligent terminal and the like are responsible for processing the bottom layer data at the near end, reducing the cloud pressure and improving the system reliability, but the structure is carried out by adopting a single wireless network architecture and is not suitable for industrial production environments with serious electromagnetic interference. The patent application numbers are: 201911144476.1, the name is: an EtherCAT-TSN industrial Ethernet architecture system comprises the following specific contents: the Ethernet switch is equivalent to a wired network centralized controller provided with a 4G wireless transmission module, wired communication and wireless transmission of the Ethernet switch are parallel two independent processes without coupling, and the Ethernet switch is high in difficulty and heavy in calculation burden and cannot be applied to a wide industrial scene. The patent application numbers are: 201710189369.5, the name is: a TSN service-oriented multi-VCPU self-adaptive real-time scheduling method comprises the following specific contents: aiming at the problem that the traditional TSN switch only has a single switching Function, a bottom layer physical resource is virtualized based on a Network Function Virtualization (NFV) technology, a uniform interface is provided for upper layer scheduling, a self-adaptive deadline priority scheduling algorithm is provided, and the requirement of routing of data streams with different priorities among a plurality of TSN switches is met.
Therefore, most of the existing communication architectures are single wired communication or wireless communication architectures, and a heterogeneous time-sensitive network architecture which combines wired communication and wireless transmission together by combining the characteristics of industrial field data is still few, and further research is urgently needed. The TSN-based communication architecture and scheduling technology are mostly based on a single switching unit or adopt SDN, NFV and other technologies to carry out comprehensive virtual resource scheduling, which greatly aggravates the calculation burden of centralized allocation of a controller, is not suitable for scenes of intelligent manufacturing real-time communication and accurate control, and the research of applying the TSN technology to a cloud-edge-end three-layer architecture communication network is less, and the functions of the TSN switch are further expanded and utilized. The research on the TSN technology focuses on a Time aware gate (TAS) mechanism of the IEEE 802.1Qbv technology, or a Precision clock synchronization mechanism (PTP) of the IEEE 802.1AS, and the research on the PSFP mechanism based on the IEEE 802.1Qci Protocol to perform queue mapping to realize multi-priority data transmission is still blank, and has a large research space. Research combining SDN and TSN technologies only focuses on the case where underlying communication is wired TSN, and there is no research combining wireless-wired heterogeneous networks performed in a wireless communication manner and a mechanism related to TSN technologies.
Therefore, those skilled in the art are devoted to develop a TSN-based software-defined industrial heterogeneous time-sensitive network architecture and resource hierarchical scheduling method. The intelligent manufacturing production scene facing the industry 4.0 is combined with the advantages of a wired TSN, wireless communication and an SDN, the adaptive reliable transmission and flexible hierarchical scheduling of multi-QoS data are realized based on an industrial wireless-wired heterogeneous time sensitive network architecture defined by software, and the low-delay high-reliability communication requirement of small-batch customized flexible production is met. The method is characterized in that a multi-priority time-sensitive transmission mechanism considering the shared time-frequency resources of burst data is designed for the characteristics of coexistence of massive heterogeneous data in an industrial field, dynamic occurrence of high-priority safety alarm information, mutual coupling of industrial field processes, sequential triggering of nodes and the like, so that deterministic transmission of high-priority data and self-adaptive reservation of communication resources are realized, the resource utilization rate is improved, and wireless communication time delay is reduced. The PSFP mechanism based on the IEEE 802.1Qci protocol is combined with the QoS requirement of data, a TSN gateway queue injection mechanism is designed, and the full-flow time sensitive communication of the wireless-wired industrial heterogeneous network is realized.
Disclosure of Invention
In view of the above defects in the prior art, the technical problem to be solved by the present invention is to make up the problems of single industrial communication mode, different QoS requirements of industrial field mass data, and the like, and to provide an intelligent manufacturing-oriented TSN-based software-defined industrial heterogeneous time-sensitive network architecture and a predictive resource scheduling method, which can realize deterministic delivery of industrial heterogeneous data as required.
In order to achieve the purpose, the invention provides a software-defined industrial heterogeneous time-sensitive network system, which comprises an industrial field layer, an SDTSN transmission layer and an industrial cloud platform, wherein the SDTSN transmission layer is used for transmitting a software-defined industrial heterogeneous time-sensitive network;
the industrial field layer comprises a communication node, a controller, an actuator and a sensor, and is communicated through a 5G technology, field data is collected and uploaded, and upper layer data is received to perform execution feedback;
the SDTSN transmission layer comprises a TSN switch supporting SDN technology and TSN intelligent gateways, the TSN intelligent gateways are mutually connected through a TSN backbone network built by the TSN switch, multi-priority wired TSN communication can be carried out, and data uploading and issuing, data cleaning and light processing are carried out;
the industrial cloud platform comprises a cloud server which has strong computing and storing capacity and gives consideration to functions of an SDN controller and a TSN control platform, is connected with a TSN routing layer through a TSN backbone network, collects cleaned bottom data, centrally controls data of the whole factory, performs large-time-scale slow feedback, and performs overall information gathering, computing and feedback.
Further, the method comprises wireless communication and wired routing, after massive field data are collected by a clustered sensor, a 5G technology is adopted to carry out relay through a communication node of a cluster where the sensor is located, and finally, bottom layer data are transmitted to a corresponding TSN intelligent gateway in a wireless mode, the data are routed to the TSN intelligent gateway to which a target controller belongs through a multi-hop wired TSN network, the TSN intelligent gateway issues the data, and finally, the controller carries out feedback control according to received sensing data.
Furthermore, the TSN intelligent gateway supports a TSN protocol and an SDN technology, each gateway access port comprises a plurality of queues with different priorities and a corresponding time perception gate TAS, hierarchical wired scheduling in a TSN backbone network is carried out, a routing flow table issued by an upper layer is analyzed, corresponding data forwarding is carried out, and flexible communication scheduling is formed; the TSN intelligent gateway has the functions of a 5G micro base station, has certain computing processing capacity, is respectively responsible for collecting and cleaning part of bottom layer equipment data, and uploads the cleaned data to the industrial cloud platform while computing processing.
The invention also provides a software-defined industrial heterogeneous time-sensitive network resource scheduling method, which comprises the following steps:
step 1, a multi-priority wireless scheduling mechanism considering burst data based on a 5G technology;
and step 2, a wired TSN multi-priority queue mapping and scheduling mechanism.
Further, the step 1 is based on a multi-priority wireless scheduling mechanism of 5G technology considering burst data, and comprises the following steps:
step 1.1, dividing industrial field data into different transmission priorities, wherein the highest priority is dynamically accidental safety detection type data (NS flow), the second Best priority is control type sensing data (TC flow), and the lowest priority is slowly-changing sensing data (Best Effort flow);
step 1.2, dividing limited time-frequency resources of an industrial field into Resource Blocks (RB), wherein each RB is a minimum irreparable unit, the corresponding frequency bandwidth is the minimum bandwidth which accords with the Nyquist interval, and the corresponding time length is a sub-time slot;
step 1.3, when each period starts, node triggering probability prediction in the period is carried out according to historical triggering information, and node predictive reservation and semi-persistent scheduling are carried out according to the prediction probability;
step 1.4, the rest nodes carry out normal triggering in a normal scheduling area, and the transmission time delay comprises signaling time and data transmission time;
step 1.5, if the safety monitoring sensor is suddenly triggered, the NS flow generated by the safety monitoring sensor is immediately scheduled in the RB fixedly reserved in the next sub-slot, and the transmission delay is reduced;
step 1.6, after all the high-priority TC (including accidental NS flow) is transmitted to the corresponding TSN intelligent gateway, the low-priority BE flow is normally scheduled and transmitted as best as possible until one transmission period is finished, and the step 1.3 is returned to BE executed.
Further, in step 1.2, the sub-slot corresponds to a transmission time length of an RB, each sub-slot includes a plurality of RBs with different frequency bands, and each sub-slot at least fixedly reserves one RB (R)f) For transmitting dynamic sporadic safety monitoring class data (NS flow) and selecting nodes with higher triggering probability to be placed in a reserved RB region FrThe above.
Further, the step 1.3 of predicting the node trigger probability comprises the following steps:
step 1.3.1: triggering a node set at the last moment, wherein A is { a ═ a1,a2,a3… …, obtaining conditional probabilities of nodes and the rest nodes in the node set A respectively by counting the prior probability P (a) triggered by the nodes in the node set A and the probability P (ab) triggered by the node set A and the rest nodes together
Figure BDA0002842443470000051
Step 1.3.2: obtaining Mutual Information (MI) using conditional probability
Figure BDA0002842443470000052
Step 1.3.3: solving for x by using conditional probability2Probability of examination (Chi-Square Test)
Figure BDA0002842443470000053
Step 1.3.4: according to the obtained conditional probability, mutual information and x2Checking probability to judge the correlation between other nodes and the node in A, and selecting the front R with the maximum correlationAReserved node set B with nodes forming node set AAWherein node set B is reservedASize RABy the number of reserved RBs per cycle RrDetermine and satisfy RA≤Rr。
Further, in step 1.5, the maximum transmission delay of the NS stream is less than or equal to twice RB corresponding time DNs≤2tRBAfter the burst NS flow is embedded into the shared time-frequency resource of the communication network, the priority is consistent with that of the TC flow.
Further, in step 1.5, the fixed reservation RB is distributed in each sub-slot of the period, and includes a sub-slot of the reservation region, and when no burst NS stream arrives, the reservation region F is locatedrFixed reservation of RB (R)f) Normal reservation can be made, if the reservation node trigger and burst NS flow arrive at the same time, burst NS flow seizes RB of reservation node, reservation node turns to RB region F for transmitting normal scheduling node signalingsAnd carrying out normal signaling communication and dynamic scheduling.
Further, the step 2 wired TSN multi-priority queue mapping and scheduling mechanism includes the following steps:
step 2.1: calculating the transmission time difference of each queue of the TSN:
Figure BDA0002842443470000054
step 2.2: waiting for wireless data to arrive;
step 2.3: calculating transmission deadline of each queue of the TSN: Λ (Q (T)) ═ Ttsn,min+Q(t)△,Q(t)＝1,2,…,x；
Step 2.4: if the wireless data arrive, uploading the wireless data QoS requirement and the destination node information to a cloud server, designing an end-to-end SDN flow table by an SDN controller, optionally setting the number of data priority queues by a TSN mechanism, and referring to the number of TSN gateway queues by x;
step 2.5: calculating the total transmission time T of the TC flow (including the NS flow) in the wireless transmission stage5G(T) in combination with T5G(t) calculating the sequence number of the TSN queue to be mapped by the TC flow in the period according to the time delay requirement of the TC flow: q (t) ═ argmin1≤Q(t)≤x(Ttsn(t)-(Tddl(t)-T5G(t)))；
Wherein, T5G(T) is obtained from the time difference between the transmission completion time of the last TC stream data packet and the cycle start time, Tddl(t) is the transmission delay requirement of TC, and Q (t) is the TSN queue serial number distributed by the TC flow in the period;
step 2.6: the TSN intelligent gateway receives and analyzes an end-to-end SDN flow table;
step 2.7: and (3) performing wired TSN gateway queue injection and routing transmission to the target TSN switch according to the flow table, and returning to the step 2.2.
In a preferred embodiment of the invention, the following technical scheme is adopted:
a Time-Sensitive network (SDTSN) industrial wireless-wired heterogeneous network architecture combined with a Software definition technology is provided, which comprises the following parts:
industrial field layer: the system mainly comprises a plurality of field devices such as communication nodes, controllers, actuators and sensors, wherein the devices are communicated through a 5G technology and used for acquiring field data, gathering and uploading the data or receiving upper layer data to perform feedback.
SDTSN transport layer: the system mainly comprises a TSN switch supporting the SDN technology and TSN intelligent gateways, the gateways are connected through a TSN backbone network built by the TSN switch, each gateway has the functions of the TSN switch and a 5G micro base station at the same time and has certain computing processing capacity, each gateway is responsible for collecting and cleaning part of bottom layer equipment data, and the cleaned data are uploaded to a cloud platform during computing processing.
An industrial cloud platform: the system is composed of a cloud server which has strong computing and storing capacity and gives consideration to functions of an SDN controller and a TSN control platform, the cloud server is connected with an SDTSN transmission layer through a TSN backbone network, cleaned bottom data are collected, data of the whole factory are managed and controlled in a centralized mode, end-to-end routing flow tables of data with different priorities are designed based on an OpenFlow protocol and are issued to a TSN gateway, bounded transmission delay is determined under the support of the TSN protocol, meanwhile, large-time-scale slow adjustment is conducted, and overall information gathering, computing and feedback are achieved.
The whole industrial network architecture can be divided into two parts, namely wireless communication and wired routing, after massive field data are collected by a clustered sensor, a 5G technology is adopted to carry out relay through a communication node of a cluster where the sensor is located, and finally, bottom layer data are transmitted to a corresponding TSN intelligent gateway in a wireless mode, the data are routed to the TSN intelligent gateway accessed by a target controller through a multi-hop wired TSN network, the TSN intelligent gateway issues the data, and finally, the controller carries out feedback control according to received sensing data.
The transmission is used as a pivot for associating sensing data and controlling feedback, and the timely and reliable delivery of the data is about the stable operation of the whole industrial process and is the key point for realizing the integration of sensing, sensing and controlling.
Due to the fact that the industrial field is distributed with a large number of sensors with different functional sites, different QoS requirements of different data are determined by different collection positions, collection contents and data types of the sensors, and the industrial wireless-wired heterogeneous network needs to achieve on-demand deterministic delivery of various types of QoS-different data.
The TSN intelligent gateway supports a TSN protocol and an SDN technology, each gateway access port comprises a plurality of queues with different priorities and a corresponding time perception gate TAS, hierarchical wired scheduling inside a TSN backbone network can be achieved, routing flow tables issued by an upper layer can be analyzed, corresponding data forwarding can be carried out, and an elastic flexible communication network is formed.
The cloud server collects QoS requirements and destination node information of various types of data transmitted from bottom to top, accordingly, a routing path of the data entering the TSN wired network is designed, a routing flow table is issued to the TSN intelligent gateway in the middle layer from top to bottom, and optimal scheduling of heterogeneous data in the wired TSN network based on global information is achieved.
A resource grading scheduling method is provided, which comprises a multi-priority wireless scheduling mechanism considering burst data based on a 5G technology and a wired TSN multi-priority queue mapping and scheduling mechanism.
A multi-priority wireless scheduling mechanism considering burst data based on 5G technology comprises the following steps:
step 1: dividing industrial field data into different transmission priorities, wherein the highest priority is dynamically and sporadically transmitted safety detection type data (Non-scheduled Traffic, NS stream), the second Best priority is control type sensing data (Time-Critical Traffic, TC stream), and the lowest priority is slowly-changing sensing data (Best Effort Traffic, BE stream)
Step 2: dividing the limited time-frequency resources of the industrial field into Resource Blocks (RB), wherein each RB is a minimum irrevocable unit, the corresponding frequency bandwidth is the minimum bandwidth which accords with the Nyquist interval, the corresponding time length is the minimum time unit, namely a sub-time slot, taking a 5G n79 frequency band as an example, the frequency band is 4800 MHz-5000 MHz, and the corresponding time length of one RB is 2 ns-2.08 ns.
And step 3: and when each period starts, node triggering probability prediction in the period is carried out according to historical triggering information, and node predictive reservation and semi-persistent scheduling are carried out according to the prediction probability.
And 4, step 4: the rest nodes are in the normal scheduling area FsAnd carrying out normal triggering, wherein the transmission delay comprises signaling time and data transmission time.
And 5: if the safety monitoring sensor is suddenly triggered, the NS stream generated by the safety monitoring sensor is immediately scheduled by the RB fixedly reserved in the next sub-slot, and the transmission delay is reduced.
Step 6: and (3) on the basis of ensuring that all the high-priority TC (including accidental NS flows) are transmitted to the corresponding TSN intelligent gateways, normally scheduling the low-priority BE flows and transmitting the low-priority BE flows as best as possible until one transmission period is finished, and returning to execute the step 3.
In step 2, the adjacent frequency bands are flat fading and homogeneous, and the transmission performance is the same. The time frequency resource is used periodically, the RBs in different stages are used for transmitting different data, and in the time axis direction, the functions of the RBs are as follows in sequence: RB region F for transmitting predictive reservation nodes (TC streams)rRB region F for transmitting normal scheduling node signalingsRB region F for transmitting normal scheduling node data (TC stream)cRB region F for transmitting Low priority nodes (BE flows)B. The sub-time slots correspond to the transmission time length of one RB, each sub-time slot comprises a plurality of RBs with different frequency bands, and each sub-time slot at least fixedly reserves one RB (R)f) For transmitting dynamic sporadic safety monitoring class data (NS flow) and selecting nodes with higher triggering probability to be placed in a reserved RB region FrThe above.
In step 5, the maximum transmission delay of the NS flow is less than or equal to twice RB corresponding time DNs≤2tRB. The fixed reservation RB is distributed in each sub-slot of the period and comprises the sub-slot of a reservation region, and when no burst NS stream arrives, the reservation region FrFixed reservation of RB (R)f) Normal reservation can be made, if the reservation node trigger and burst NS flow arrival occur simultaneously, the burst NS flow seizes the RB of the reservation node, which in turn is at FsThe area carries out normal signaling communication and dynamic scheduling. After the burst NS flow is embedded into the shared time-frequency resource of the communication network, the priority of the burst NS flow is consistent with that of the TC flow.
A wired TSN multi-priority queue mapping and scheduling mechanism comprises the following steps:
step 1: calculating the transmission time difference of each queue of the TSN:
Figure BDA0002842443470000081
step 2: waiting for wireless data to arrive.
And step 3: calculating transmission deadline of each queue of the TSN: Λ (Q (T)) ═ Ttsn,min+Q(t)△,Q(t)＝1,2,…,x。
And 4, step 4: and if the wireless data arrives, uploading the wireless data QoS requirement and the destination node to a cloud server to design an end-to-end SDN flow table.
And 5: calculating the total transmission time T of the TC flow (including the NS flow) in the wireless transmission stage5G(T) in combination with T5G(t) calculating the sequence number of the TSN queue to be mapped by the TC flow in the period according to the time delay requirement of the TC flow: q (t) ═ argmin1≤Q(t)≤x(Ttsn(t)-(Tddl(t)-T5G(t)))。
Step 6: the TSN intelligent gateway receives and analyzes an end-to-end SDN flow table.
And 7: and (3) performing wired TSN gateway queue injection and routing transmission to the target TSN switch according to the flow table, and returning to the step (2).
In step 3, since the TSN mechanism can optionally make the number of data priority queues, x refers to the number of TSN gateway queues.
In step 5, said T5GAnd (t) is obtained by the time difference from the transmission completion time of the last TC stream data packet to the cycle start time, and Q (t) is the TSN queue number allocated to the TC stream of the cycle.
Compared with the prior art, the invention has the following obvious substantive characteristics and obvious advantages:
1. the wireless sensing and control integrated intelligent transmission system has the advantages that the advantages of wireless communication and a wired TSN are innovatively combined, large-range wireless sensing and near-end time sensitive transmission in a wide area of a factory are realized, and the intelligent manufacturing of integration of sensing, sensing and control is facilitated.
2. By combining the current situations of time-frequency resource shortage and different data QoS in an industrial field, a wireless scheduling mechanism facing multi-priority data streams and sudden key data streams is designed, and the transmission requirements of different data are met.
3. By utilizing an IEEE 802.1Qci mechanism of a wired TSN protocol, the mapping relation between a wired TSN queue and transmission time is innovatively modeled, and the TSN queue mapping and the hierarchical time sensitive transmission of a wired network are realized.
4. The wired TSN transmission is based on wireless transmission, and overtime and jitter caused by the wireless transmission can be compensated through queue selection, so that the full-flow time-sensitive hierarchical scheduling of the industrial wireless-wired heterogeneous network is realized.
5. The characteristics of mutual coupling of industrial field processes and sequential triggering of nodes are fitted, the correlation among the nodes is predicted based on historical information, resource reservation is carried out in advance, communication handshake delay in the dynamic scheduling process is greatly reduced, and semi-persistent scheduling and low-delay communication are achieved.
6. By adopting a software defined network technology, the data streams with different QoS requirements are dispatched in a centralized manner based on the global information of the network, so that the time certainty and the resource utilization rate of the TSN network are greatly improved.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a SDTSN diagram of the SDN architecture according to the preferred embodiment of the present invention;
FIG. 2 is a software defined heterogeneous network SDTSN communication flow diagram of a preferred embodiment of the present invention;
FIG. 3 is a flow chart of a wireless multi-priority scheduling mechanism in accordance with a preferred embodiment of the present invention;
FIG. 4 is a wireless time-frequency resource block map of a preferred embodiment of the present invention;
fig. 5 is a flow chart of the wired TSN queue injection and way scheduling mechanism according to a preferred embodiment of the present invention.
The system comprises a TSN intelligent gateway 1, an actuator 2, a sensor 3 and a communication node 4.
Detailed Description
The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
In the drawings, structurally identical elements are represented by like reference numerals, and structurally or functionally similar elements are represented by like reference numerals throughout the several views. The size and thickness of each component shown in the drawings are arbitrarily illustrated, and the present invention is not limited to the size and thickness of each component. The thickness of the components may be exaggerated where appropriate in the figures to improve clarity.
As shown in fig. 1, a software-defined industry heterogeneous time-sensitive network architecture SDTSN based on TSN includes the following parts:
industrial field layer: the system mainly comprises a plurality of field devices such as a communication node 4, a controller, an actuator 2 and a sensor 3, wherein the devices are communicated through a 5G technology and used for collecting field data and gathering and uploading the data or receiving upper layer data to execute feedback.
SDTSN transport layer: the system mainly comprises a TSN switch supporting SDN technology and a TSN intelligent gateway 1, wherein the gateways are mutually connected through a TSN backbone network built by the TSN switch, multi-priority wired TSN communication can be performed, and the functions of uploading and issuing, data cleaning and light processing of a communication hub are achieved in the whole network architecture.
An industrial cloud platform: the system is composed of a cloud server which has high computing and storing capacity and gives consideration to functions of an SDN controller and a TSN control platform, is connected with a TSN routing layer through a TSN backbone network, collects cleaned bottom data, centrally controls data of the whole factory, performs large-time-scale slow feedback, and achieves overall information gathering, computing and feedback.
The industrial network architecture can be divided into two parts, namely wireless communication and wired routing, after massive field data are collected by a clustered sensor, a 5G technology is adopted to carry out relay through a communication node 4 of a cluster where the sensor is located, and finally, bottom layer data are transmitted to a corresponding TSN intelligent gateway 1 in a wireless mode, the data are routed to the TSN intelligent gateway 1 to which a target controller belongs through a multi-hop wired TSN network, the TSN intelligent gateway 1 issues the data, and finally, the controller carries out feedback control according to received sensing data. Due to the fact that the industrial field is distributed with a large number of sensors with different functional places, different QoS requirements of different data are determined by different collection positions, collection contents and data types of the sensors, and the industrial wireless-wired heterogeneous network can achieve deterministic delivery of various QoS-different data according to needs.
The TSN intelligent gateway 1 supports a TSN protocol and an SDN technology, each gateway access port comprises a plurality of queues with different priorities and a corresponding time perception gate TAS, hierarchical wired scheduling in a TSN backbone network can be achieved, routing flow tables issued by an upper layer can be analyzed, corresponding data forwarding can be carried out, and flexible communication scheduling is formed. Meanwhile, the TSN gateway has the functions of a 5G micro base station, has certain computing processing capacity, is respectively responsible for collecting and cleaning part of bottom layer equipment data, and uploads the cleaned data to the cloud platform while computing processing.
A resource grading scheduling method comprises a multi-priority wireless scheduling mechanism considering burst data based on a 5G technology and a wired TSN multi-priority queue mapping and scheduling mechanism.
A 5G technology-based multi-priority wireless scheduling mechanism considering burst data, comprising the steps of:
step 1.1: dividing industrial field data into different transmission priorities, wherein the highest priority is dynamically and sporadically transmitted safety detection type data (Non-scheduled Traffic, NS stream), the second Best priority is control type sensing data (Time-Critical Traffic, TC stream), and the lowest priority is slowly-changing sensing data (Best Effort Traffic, BE stream)
Step 1.2: dividing the limited time-frequency resources of the industrial field into Resource Blocks (RB), wherein each RB is a minimum irrevocable unit, the corresponding frequency bandwidth is the minimum bandwidth which accords with the Nyquist interval, the corresponding time length is the minimum time unit, namely a sub-time slot, taking a 5G n79 frequency band as an example, the frequency band is 4800 MHz-5000 MHz, and the corresponding time length of one RB is 2 ns-2.08 ns.
Step 1.3: and when each period starts, node triggering probability prediction in the period is carried out according to historical triggering information, and node predictive reservation and semi-persistent scheduling are carried out according to the prediction probability.
Step 1.4: the rest nodes are in the normal scheduling area FsAnd carrying out normal triggering, wherein the transmission delay comprises signaling time and data transmission time.
Step 1.5: if the safety monitoring sensor is suddenly triggered, the NS stream generated by the safety monitoring sensor is immediately scheduled by the RB fixedly reserved in the next sub-slot, and the transmission delay is reduced.
Step 1.6: after the high-priority TC (including the sporadic NS stream) is all transmitted to the corresponding TSN intelligent gateway, the low-priority BE stream is normally scheduled and transmitted as best as possible until one transmission cycle is finished, and the step 1.3 is returned to BE executed.
In step 1.2, the time-frequency resource is used periodically, RBs at different stages are used for transmitting different data, and in the time axis direction, the functions of the RBs are as follows: RB region F for transmitting predictive reservation nodes (TC streams)rRB region F for transmitting normal scheduling node signalingsRB region F for transmitting normal scheduling node data (TC stream)cRB region F for transmitting Low priority nodes (BE flows)B。
In step 1.2, the sub-slots correspond to the transmission time length of one RB, each sub-slot includes a plurality of RBs with different frequency bands, and each sub-slot at least fixedly reserves one RB (R)f) For transmitting dynamic sporadic safety monitoring class data (NS flow) and selecting nodes with higher triggering probability to be placed in a reserved RB region FrThe above.
Step 1.3 the node probability prediction method includes the following steps:
step 1.3.1: the trigger node set a at the last moment is { a ═ a1,a2,a3… …, by counting the prior probability P (a) of node triggering in Ai) And the probability P (a) that the node set A and the rest of the nodes trigger togetheri,bj) Respectively obtaining the conditional probability of the nodes in the node set A and the conditional probability of the residual nodes
Figure BDA0002842443470000111
Step 1.3.2: obtaining Mutual Information (MI) using conditional probability
Figure BDA0002842443470000112
Step 1.3.3: solving for x by using conditional probability2Probability of examination (Chi-Square Test)
Figure BDA0002842443470000113
Step 1.3.4: according to the obtained conditional probability, mutual information and x2Checking probability to judge the correlation between other nodes and the node in A, and selecting the front R with the maximum correlationAReserved node set B with nodes forming node set AAWherein node set B is leftASize RABy the number of reserved RBs per cycle RrDetermine and satisfy RA≤Rr。
In step 1.5, the maximum transmission delay of the NS stream is less than or equal to twice RB corresponding time DNs≤2tRBAnd after the burst NS flow is embedded into the shared time-frequency resource of the communication network, the priority of the burst NS flow is consistent with that of the TC flow.
In step 1.5, the fixed reservation RB is distributed in each sub-slot of the cycle, including the sub-slot of the reservation field, and when no burst NS stream arrives, the reservation field F is set to be equal to the reserved field FrFixed reservation of RB (R)f) Normal reservation can be made, if the reservation node trigger and burst NS flow arrival occur simultaneously, the burst NS flow seizes the RB of the reservation node, which in turn is at FsThe area carries out normal signaling communication and dynamic scheduling.
A resource grading scheduling method comprises a multi-priority wireless scheduling mechanism considering burst data based on a 5G technology and a wired TSN multi-priority queue mapping and scheduling mechanism. A wired TSN multi-priority queue mapping and scheduling mechanism, comprising the steps of:
step 2.1: calculating the transmission time difference of each queue of the TSN:
Figure BDA0002842443470000114
step 2.2: waiting for wireless data to arrive.
Step 2.3: calculating transmission deadline of each queue of the TSN: Λ (Q (T)) ═ Ttsn,min+Q(t)△,Q(t)＝1,2,…,x
Step 2.4: if the wireless data arrives, the wireless data QoS requirement and the destination node information are uploaded to a cloud server, an SDN controller carries out end-to-end SDN flow table design according to the wireless data QoS requirement and the destination node information, and the number of data priority queues can be optionally set by a TSN mechanism, so that x refers to the number of TSN gateway queues.
Step 2.5: calculating the total transmission time T of the TC flow (including the NS flow) in the wireless transmission stage5G(T) in combination with T5G(t) calculating the sequence number of the TSN queue to be mapped by the TC flow in the period according to the time delay requirement of the TC flow: q (t) ═ argmin1≤Q(t)≤x(Ttsn(t)-(Tddl(t)-T5G(t))). The T is5G(T) is obtained from the time difference from the transmission completion time of the last TC stream data packet to the cycle start time, and the TddlAnd (t) is the transmission delay requirement of the TC, and Q (t) is the sequence number of the TSN queue allocated to the TC flow in the period.
Step 2.6: receiving and analyzing end-to-end SDN flow table by TSN intelligent gateway
Step 2.7: and (4) performing wired TSN gateway queue injection and routing transmission to the target TSN switch according to the flow table, and returning to the step 2.2.
The invention can adopt 5G technology to carry out wireless communication, and can also adopt WielessHART, Zigbee and other wireless technologies to carry out field communication.
The communication flow of the whole heterogeneous network is shown in fig. 2, and the mapping relationship of the time-frequency resource blocks of the wireless scheduling is shown in fig. 4.
Taking a steel plant as an example, the main process is a hot rolling production line, and a software defined industrial wireless (5G) -wired (TSN) heterogeneous network three-layer framework diagram consists of the following three parts:
industrial field layer: the device mainly comprises a plurality of field devices such as a communication node 4, a PLC, a roller, a temperature sensor, a vibration sensor, a camera, a pressure gauge and a humidity sensor, wherein the field devices are communicated through a 5G technology and used for collecting hot rolling field data and summarizing and uploading the hot rolling field data or receiving local cloud platform data to perform feedback.
SDTSN transport layer: the system mainly comprises TSN switches supporting SDN technology and TSN intelligent gateways 1 distributed on an industrial field, the gateways are in wired connection through a TSN backbone network built by the TSN switches, multi-priority wired TSN communication can be performed, each TSN gateway manages data communication and control feedback of at least one production workshop, and communication hub functions of uploading and issuing, data cleaning and light processing are achieved in the whole network architecture.
An industrial cloud platform: the system is composed of a cloud server which has strong computing and storing capacity and gives consideration to functions of an SDN controller and a TSN control platform, the cloud server is connected with a TSN routing layer through a TSN backbone network, cleaned bottom data are collected, data of the whole hot rolling factory are managed and controlled in a centralized mode, end-to-end communication network topologies of data with different priorities are designed, bounded transmission delay is determined under the support of a TSN protocol, meanwhile, large-time-scale slow feedback is carried out, control feedback such as global perception information summarization, computational learning and rolling is achieved, and data interaction, instruction execution and network topology of the whole factory are managed in a centralized mode.
As shown in fig. 3, a 5G technology-based multi-priority wireless scheduling mechanism considering burst data includes the following steps:
step 1: dividing industrial field data into different transmission priorities, wherein the highest priority is dynamically and sporadically transmitted safety detection type data (Non-scheduled Traffic, NS stream), the second Best priority is control type sensing data (Time-Critical Traffic, TC stream), and the lowest priority is slowly-changing sensing data (Best Effort Traffic, BE stream)
Step 2: dividing the limited time-frequency resources of the industrial field into Resource Blocks (RB), wherein each RB is a minimum irrevocable unit, the corresponding frequency bandwidth is the minimum bandwidth which accords with the Nyquist interval, the corresponding time length is the minimum time unit, namely a sub-time slot, taking a 5G n79 frequency band as an example, the frequency band is 4800 MHz-5000 MHz, and the corresponding time length of one RB is 2 ns-2.08 ns.
And step 3: and when each period starts, node triggering probability prediction in the period is carried out according to historical triggering information, and node predictive reservation and semi-persistent scheduling are carried out according to the prediction probability.
And 4, step 4: the node probability prediction method comprises the following steps:
step 4.1: the trigger node set a at the last moment is { a ═ a1,a2,a3… …, by counting the prior probability P (a) of node triggering in Ai) And the probability P (a) that the node set A and the rest of the nodes trigger togetheri,bj) Respectively obtaining the conditional probability of the nodes in the node set A and the conditional probability of the residual nodes
Figure BDA0002842443470000121
Step 4.2: obtaining Mutual Information (MI) using conditional probability
Figure BDA0002842443470000131
Step 4.3: solving for x by using conditional probability2Probability of examination (Chi-Square Test)
Figure BDA0002842443470000132
Step 4.4, according to the obtained conditional probability, mutual information and x2Checking probability to judge the correlation between other nodes and the node in A, and selecting the front R with the maximum correlationAReserved node set B with nodes forming node set AAWherein node set B is leftASize RABy the number of reserved RBs per cycle RrDetermine and satisfy RA≤Rr。
And 5: the rest nodes are in the normal scheduling area FsAnd carrying out normal triggering, wherein the transmission delay comprises signaling time and data transmission time.
Step 6: if the safety monitoring sensor is suddenly triggered, the NS stream generated by the safety monitoring sensor is immediately scheduled by the RB fixedly reserved in the next sub-slot, and the transmission delay is reduced.
And 7: after the high-priority TC (including the accidental NS flow) is completely transmitted to the corresponding TSN intelligent gateway, the low-priority BE flow is normally scheduled and transmitted as best as possible until one transmission period is finished, and the step 3 is returned to BE executed.
As shown in fig. 5, a wired TSN multi-priority queue mapping and scheduling mechanism includes the following steps:
step 1: calculating the transmission time difference of each queue of the TSN:
Figure BDA0002842443470000133
step 2: waiting for wireless data to arrive.
And step 3: calculating transmission deadline of each queue of the TSN: Λ (Q (T)) ═ Ttsn,min+Q(t)△,Q(t)＝1,2,…,x
And 4, step 4: and if the wireless data arrives, uploading the wireless data QoS requirement and the destination node to a cloud server to design an end-to-end SDN flow table.
And 5: calculating the total transmission time T of the TC flow (including the NS flow) in the wireless transmission stage5G(T) in combination with T5G(t) calculating the sequence number of the TSN queue to be mapped by the TC flow in the period according to the time delay requirement of the TC flow: q (t) ═ argmin1≤Q(t)≤x(Ttsn(t)-(Tddl(t)-T5G(t)))。
Step 6: the TSN intelligent gateway receives and analyzes an end-to-end SDN flow table.
And 7: and (3) performing wired TSN gateway queue injection and routing transmission to the target TSN switch according to the flow table, and returning to the step (2).
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (10)

1. A software-defined industrial heterogeneous time-sensitive network system is characterized by comprising an industrial field layer, an SDTSN transmission layer and an industrial cloud platform;
the industrial field layer comprises a communication node, a controller, an actuator and a sensor, and is communicated through a 5G technology, field data is collected and uploaded, and upper layer data is received to perform execution feedback;
the SDTSN transmission layer comprises a TSN switch supporting SDN technology and TSN intelligent gateways, the TSN intelligent gateways are mutually connected through a TSN backbone network built by the TSN switch, multi-priority wired TSN communication can be carried out, and data uploading and issuing, data cleaning and light processing are carried out;
the industrial cloud platform comprises a cloud server which has strong computing and storing capacity and gives consideration to functions of an SDN controller and a TSN control platform, is connected with a TSN routing layer through a TSN backbone network, collects cleaned bottom data, centrally controls data of the whole factory, performs large-time-scale slow feedback, and performs overall information gathering, computing and feedback.
2. The software-defined industrial heterogeneous time-sensitive network system of claim 1, comprising wireless communication and wired routing, wherein after massive field data are collected by the clustered sensors, the clustered sensors are relayed by the communication nodes of the clusters where the clustered sensors are located by adopting a 5G technology, and finally, bottom layer data are wirelessly transmitted to the corresponding TSN intelligent gateways, the data are routed to the TSN intelligent gateways to which the target controllers belong by the multi-hop wired TSN network, the TSN intelligent gateways issue the data, and finally, the controllers perform feedback control according to the received sensing data.
3. The software-defined industrial heterogeneous time-sensitive network system of claim 1, wherein the TSN smart gateway supports TSN protocol and SDN technology, each gateway access port comprises a plurality of queues with different priorities and corresponding time aware gates TAS, performs hierarchical cable scheduling inside a TSN backbone network, parses a routing flow table issued by an upper layer, and performs corresponding data forwarding; the TSN intelligent gateway has the functions of a 5G micro base station, has certain computing processing capacity, is respectively responsible for collecting and cleaning partial bottom layer equipment data, and uploads the cleaned data to the cloud platform while computing processing.
4. A software-defined industrial heterogeneous time-sensitive network resource scheduling method is characterized by comprising the following steps:
step 1, a multi-priority wireless scheduling mechanism considering burst data based on a 5G technology;
and step 2, a wired TSN multi-priority queue mapping and scheduling mechanism.
5. The software defined industrial heterogeneous time sensitive network resource scheduling method according to claim 4, wherein said step 1 is based on a multi-priority wireless scheduling mechanism of 5G technology considering burst data, comprising the steps of:
step 1.1, dividing industrial field data into different transmission priorities, wherein the highest priority is dynamically accidental safety detection type data (NS flow), the second Best priority is control type sensing data (TC flow), and the lowest priority is slowly-changing sensing data (Best Effort flow);
step 1.2, dividing limited time-frequency resources of an industrial field into Resource Blocks (RB), wherein each RB is a minimum irreparable unit, the corresponding frequency bandwidth is the minimum bandwidth which accords with the Nyquist interval, and the corresponding time length is a sub-time slot;
step 1.3, when each period starts, node triggering probability prediction in the period is carried out according to historical triggering information, and node predictive reservation and semi-persistent scheduling are carried out according to the prediction probability;
step 1.4, the rest nodes carry out normal triggering in a normal scheduling area, and the transmission time delay comprises signaling time and data transmission time;
step 1.5, if the safety monitoring sensor is suddenly triggered, the NS flow generated by the safety monitoring sensor is immediately scheduled in the RB fixedly reserved in the next sub-slot, and the transmission delay is reduced;
step 1.6, after all the high-priority TC (including accidental NS flow) is transmitted to the corresponding TSN intelligent gateway, the low-priority BE flow is normally scheduled and transmitted as best as possible until one transmission period is finished, and the step 1.3 is returned to BE executed.
6. The method as claimed in claim 5, wherein in step 1.2, the sub-slots correspond to a transmission time length of an RB, each sub-slot includes a plurality of RBs with different frequency bands, and each sub-slot at least fixedly reserves one RB (R)f) For transmitting dynamic sporadic safety monitoring class data (NS flow) and selecting nodes with higher triggering probability to be placed in a reserved RB region FrThe above.
7. The software-defined industrial heterogeneous time-sensitive network resource scheduling method of claim 5, wherein said step 1.3 of node trigger probability prediction comprises the steps of:
step 1.3.1: triggering a node set at the last moment, wherein A is { a ═ a1,a2,a3… … by counting the prior probability P (a) of node triggering in Ai) And the probability P (a) that the node set A and the rest of the nodes trigger togetheri,bj) Respectively obtaining the conditional probability of the nodes in the node set A and the conditional probability of the residual nodes
Figure FDA0002842443460000021
Step 1.3.2: obtaining Mutual Information (MI) using conditional probability
Figure FDA0002842443460000022
Step 1.3.3: solving for x by using conditional probability2Probability of examination (Chi-Square Test)
Figure FDA0002842443460000023
Step 1.3.4: according to the obtained conditional probability, mutual information and x2Checking probability to judge the correlation between other nodes and the node in A, and selecting the front R with the maximum correlationAReserved node set B with nodes forming node set AAWherein node set B is reservedASize RABy the number of reserved RBs per cycle RrDetermine and satisfy RA≤Rr。
8. The method as claimed in claim 5, wherein in step 1.5, the maximum transmission delay of the NS stream is less than or equal to twice RB corresponding time DNs≤2tRBAfter the burst NS flow is embedded into the shared time-frequency resource of the communication network, the priority is consistent with that of the TC flow.
9. The method as claimed in claim 6, wherein in step 1.5, the fixed reserved RB is distributed in each sub-slot of the period and includes a sub-slot of a reserved region, and when no burst NS stream arrives, the reserved region F is locatedrFixed reservation of RB (R)f) Normal reservation can be made, if the reservation node trigger and burst NS flow arrive at the same time, burst NS flow seizes RB of reservation node, reservation node turns to RB region F for transmitting normal scheduling node signalingsAnd carrying out normal signaling communication and dynamic scheduling.
10. The software-defined industrial heterogeneous time-sensitive network resource scheduling method of claim 4, wherein said step 2 wired TSN multi-priority queue mapping and scheduling mechanism comprises the steps of:
step 2.1: calculating the transmission time difference of each queue of the TSN:
Figure FDA0002842443460000031
step 2.2: waiting for wireless data to arrive;
step 2.3: calculating transmission deadline of each queue of the TSN: Λ (Q (T)) ═ Ttsn,min+Q(t)△,Q(t)＝1,2,…,x；
Step 2.4: if the wireless data arrive, uploading the wireless data QoS requirement and the destination node information to a cloud server, designing an end-to-end SDN flow table by an SDN controller, optionally setting the number of data priority queues by a TSN mechanism, and referring to the number of TSN gateway queues by x;
step 2.5: calculating the total transmission time T of the TC flow (including the NS flow) in the wireless transmission stage5G(T) in combination with T5G(t) calculating the sequence number of the TSN queue to be mapped by the TC flow in the period according to the time delay requirement of the TC flow: q (t) ═ argmin1≤Q(t)≤x(Ttsn(t)-(Tddl(t)-T5G(t)))；
Wherein, T5G(T) is obtained from the time difference between the transmission completion time of the last TC stream data packet and the cycle start time, Tddl(t) is the transmission delay requirement of TC, and Q (t) is the TSN queue serial number distributed by the TC flow in the period;
step 2.6: the TSN intelligent gateway receives and analyzes an end-to-end SDN flow table;
step 2.7: and (3) performing wired TSN gateway queue injection and routing transmission to the target TSN switch according to the flow table, and returning to the step 2.2.
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