Modbus RTU cross-TSN scheduling method based on entropy weight method and boxing algorithm

Abstract

The invention relates to a Modbus RTU cross-TSN scheduling method based on an entropy weight method and a boxing algorithm, and belongs to the field of data exchange networks. According to the method, firstly, Modbus RTU data frames are converted into Modbus TCP data frames, and data frame priorities are divided according to data types and end-to-end delay requirements; then, a fuzzy analytic hierarchy process and an entropy weight method are adopted to select a proper path for scheduling, and factors such as end-to-end delay, bandwidth and transmission period are taken as the basis of path selection; and finally, scheduling calculation of the heterogeneous network is carried out by using a two-dimensional packing algorithm, a scheduling table is generated, and transmission of a data stream is guided. According to the invention, the data transmission efficiency and the resource utilization rate of the industrial heterogeneous network are effectively improved, the delay of data transmission is reduced, and technical support is provided for industrial development.

Description

Technical Field

[0001] The invention belongs to the field of data exchange networks and relates to a ModbusRTU cross-TSN scheduling method based on an entropy weight method and a box packing algorithm. Background Art

[0002] One of the important directions of industrial development is the Industrial Internet of Things. Network technology and communication technology connect all things in the manufacturing industry so that they can be interconnected. In order to meet the different network requirements of various industrial sites, various wired network protocols and wireless network protocols have been generated. Wired networks include ModbusRTU, PRIFINET, etc. Although there are many studies on the scheduling of ModbusRTU and TSN for industrial wired networks, the problems of data transmission, resource allocation and traffic scheduling in industrial heterogeneous networks have not been well solved. In an industrial control system, if it involves tasks that require real-time control and data exchange, such as robot operation in precision manufacturing or high-speed sensor data collection, the use of ModbusRTU alone may not meet the timing requirements, while the introduction of TSN technology can ensure the precise timing of data and ensure the safety and stability of industrial processes. In industrial heterogeneous networks, the dimensions of network resources, data transmission requirements, transmission strategies and scheduling mechanisms are completely different. How to ensure low latency and deterministic latency of different types of data in industrial heterogeneous networks, the existing industrial heterogeneous network scheduling mechanism does not have a clear solution.

[0003] Effective scheduling strategies can reasonably allocate and use resources according to these characteristics, improving overall network efficiency. Improved fault tolerance and reliability. In a heterogeneous network environment, node or link failures may cause the performance of the entire system to degrade or even fail. Reasonable scheduling strategies can achieve rapid fault detection, redundant path planning, and traffic redirection, thereby enhancing the reliability and robustness of the network. With the development of the Internet of Things, Industrial Internet, and future new network technologies, the scale of networks continues to expand and the structure becomes increasingly complex. Research on heterogeneous network scheduling can provide flexible and scalable solutions to adapt to the ever-changing network environment.

[0004] The introduction of time-sensitive networking (TSN) provides a solution to the problem that traditional Ethernet cannot meet the requirements of low latency, high reliability, and high stability. Based on the framework of some proposed scheduling mechanisms, the TSN traffic scheduling algorithm proposes its own solutions for different scheduling traffic and targets, greatly optimizing the performance. In terms of time-sensitive network synchronization scheduling algorithms, Nasrallah A et al. proposed time-aware shaper (TAS) and asynchronous service shaper (ATS) mechanisms for the standard. In order to address the shortcomings of TAS, two new mechanisms, namely adaptive bandwidth sharing (ABS) and adaptive time slot window (ASW) mechanisms, were proposed. The TSN standard mechanism and new mechanism were evaluated through extensive simulations of sporadic Poisson traffic and periodic traffic. Adaptive bandwidth sharing and adaptive time slot window mechanisms were proposed and evaluated to adapt TAS to traffic fluctuations. Asynchronous traffic shaper (ATS) was further evaluated, which was proposed to provide low-latency network services without the need for time synchronization of network nodes. The evaluation shows that if the gating ratio of high-priority scheduled services (ST) is large enough to accommodate high-priority traffic, standard TAS usually achieves ultra-short latency. It is also observed that the introduced ABS mechanism improves the quality of service provided to low priority best effort (BE) traffic while maintaining ultra-short delays for high priority traffic.

[0005] In terms of industrial heterogeneous network scheduling, Wan J et al. proposed a heterogeneous network architecture based on software-defined networking to achieve cross-network flexible forwarding of multi-source manufacturing data and optimal utilization of network resources. Subsequently, the mechanism of cross-network fusion and scheduling (CNFS) was analyzed from the perspective of the high dynamic characteristics of data flows and different delay requirements. A path-aware data flow dynamic reconstruction algorithm was proposed to improve the fusion efficiency of multi-source heterogeneous data in multi-task collaboration and highly dynamic smart factory environments. Especially for multi-variable and small-batch smart manufacturing systems. In order to meet the bandwidth requirements of different delay flows, a delay-sensitive network bandwidth scheduling algorithm was proposed. In a network environment where wired / wireless multi-protocols coexist and different data flows have different real-time requirements, effective data forwarding and resource scheduling mechanisms are the key to improving the system network performance. In addition, the continuous or discrete characteristics of the data flow in the system are used to optimize and schedule the network bandwidth to meet the real-time requirements of different delay flows.

[0006] One of the key issues in industrial heterogeneous network scheduling is the generation of industrial heterogeneous network scheduling tables. Most of the current heterogeneous network scheduling algorithms solve approximate optimal solutions. The study of heterogeneous network scheduling is to meet the current smart factory's requirements for industrial Internet of Things, such as deterministic low latency and flexibility, and has certain theoretical and practical significance. Summary of the invention

[0007] In view of this, the purpose of the present invention is to provide a Modbus RTU cross-TSN scheduling method based on entropy weight method and packing algorithm. The present invention first converts the Modbus RTU data frame into a Modbus TCP data frame that can be transmitted on Ethernet; then divides the Modbus RTU cross-TSN data frame priority according to the data type of the Modbus TCP data frame and the end-to-end delay requirement of the heterogeneous network; and then uses the fuzzy hierarchical analysis method and entropy weight method to select a suitable path for heterogeneous network scheduling. Taking factors such as end-to-end delay as factors for path selection, the fuzzy hierarchical analysis method is first used to reasonably adjust the weight coefficients of different indicators to the path, and then the entropy weight method is used to calculate the entropy weight of each path to find the most suitable path for scheduling; finally, a two-dimensional packing algorithm is used to perform scheduling calculations for heterogeneous networks to generate a heterogeneous network scheduling table, and the heterogeneous network schedules traffic according to this heterogeneous network scheduling table.

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] The Modbus RTU cross-TSN scheduling method based on entropy weight method and box packing algorithm includes the following steps:

[0010] Step 1: Establish a heterogeneous network scheduling model, including TSN links and Modbus RTU links, as well as the transmitted flows;

[0011] Step 2: Convert the Modbus RTU data frame into Modbus TCP data frame;

[0012] Step 3: Prioritize Modbus RTU cross-TSN data frames according to the data type of Modbus TCP data frames and the end-to-end latency requirements of heterogeneous networks;

[0013] Step 4: Use the hierarchical analysis method and entropy weight method to select the Modbus RTU cross-TSN scheduling path, and use the end-to-end delay, bandwidth and transmission cycle as evaluation indicators for path selection;

[0014] Step 5: Use the two-dimensional bin packing algorithm to perform scheduling calculations on heterogeneous networks, generate a heterogeneous network scheduling table, and guide the transmission of data flows.

[0015] Further, in step 2, the step of converting the Modbus RTU data frame into a Modbus TCP data frame includes:

[0016] Perform CRC check on the received Modbus RTU data frame;

[0017] Extract the function code of the Modbus RTU protocol and remove the CRC check tail;

[0018] Add the MBAP message header to form the ADU unit of the Modbus TCP protocol.

[0019] Further, in step 3, the step of dividing the priority of Modbus RTU cross-TSN data frames includes:

[0020] Modbus TCP data frames are divided into three levels according to data type: security, control and monitoring;

[0021] According to the end-to-end delay requirement of data frames, data frames are divided into real-time data and non-real-time data;

[0022] The scheduling priority of the data frame is calculated based on the weight coefficient of the data type and the end-to-end delay.

[0023] Furthermore, in step 4, the step of selecting the Modbus RTU cross-TSN scheduling path using the hierarchical analysis method and the entropy weight method includes:

[0024] The analytic hierarchy process is used to determine the importance of end-to-end delay, bandwidth and transmission period to the path;

[0025] The entropy weight method is used to calculate the entropy weight of each path, and the most appropriate path is selected based on the entropy weight and scheduling priority.

[0026] Furthermore, in step 5, the step of using a two-dimensional bin packing algorithm to perform scheduling calculations on heterogeneous networks includes:

[0027] Abstract link time resources as “empty boxes” and time-triggered communication tasks as “two-dimensional objects”;

[0028] Sort the items by height and width, and put them into the "empty box" one by one;

[0029] Update the "available crate slots" queue and the item queue until all items are placed in the "box" or no more items can be placed.

[0030] The beneficial effects of the present invention are:

[0031] (1) By dividing the data frame priorities and selecting the appropriate scheduling path, the delay of data transmission is effectively reduced, meeting the low-latency requirements of industrial heterogeneous networks.

[0032] (2) Using the bin packing algorithm for resource scheduling improves the utilization of network resources and avoids resource waste.

[0033] (3) Through reasonable path selection and scheduling strategies, the reliability of the network is enhanced and the robustness of the system is improved.

[0034] (4) The present invention can be adjusted according to different network environments and data transmission requirements, and has flexibility and scalability.

[0035] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0037] Figure 1 This is the Modbus RTU scheduling architecture diagram across TSN;

[0038] Figure 2 Flowchart for converting Modbus RTU data frame to Modbus TCP data frame;

[0039] Figure 3 Schematic diagram of data frame link considering delay

[0040] Figure 4 Diagram of the relationship between the data flow transmission cycle and the basic cycle

[0041] Figure 5 For packing point;

[0042] Figure 6 Packing sorted items into boxes;

[0043] Figure 7 This is the flow chart of the bin packing algorithm. DETAILED DESCRIPTION

[0044] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0045] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0046] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0047] 1. Modbus RTU scheduling architecture across TSN

[0048] Referring to IEC / IEEE60802 and other standards, the Modbus RTU cross-TSN scheduling architecture designed in this article is shown in the figure below: Figure 1 shown.

[0049] The scheduling architecture diagram is divided into the user plane, control plane and data plane, as follows:

[0050] User plane: It is the user plane APP, responsible for data input and data output feedback;

[0051] Control plane: It consists of an industrial software-defined controller and two Modbus RTU network system managers. The industrial software-defined controller contains a collaborative scheduling subsystem and four interfaces. The collaborative scheduling subsystem uses an algorithm to calculate and generate a scheduling table based on the network topology and traffic information. The four interfaces are the northbound interface, the southbound interface, the eastbound interface, and the westbound interface. The interfaces are responsible for data interaction with the user-side APP, the westbound interface and the eastbound interface are responsible for interacting with the Modbus RTU network system manager for network status information, and the southbound interface is connected to the industrial gateway and CNC configuration interface of the Modbus RTU network on both sides of the data plane to read the industrial network data on site and issue the scheduling table.

[0052] Data plane: Both sides are composed of Modbus RTU, the left side is the Modbus RTU master station, the right side is the Modbus RTU slave station, and the middle is the TSN backbone network. Data is sent from the Modbus RTU master station on the left and forwarded by TSN to the Modbus RTU slave station.

[0053] 2. Modbus RTU cross-TSN scheduling method based on entropy weight method and box packing algorithm

[0054] 2.1 Overview of the Solution

[0055] This scheme proposes a Modbus RTU cross-TSN scheduling scheme based on the entropy weight method and the packing algorithm. The overall scheduling scheme is divided into five steps: the first step is to establish a heterogeneous network scheduling model; the second step is to convert the Modbus RTU data frame into a Modbus TCP data frame that can be transmitted on Ethernet; the third step is to divide the Modbus RTU cross-TSN data frame priority according to the data type of the Modbus TCP data frame and the end-to-end delay requirements of the heterogeneous network; the fourth step is to use the hierarchical analysis method and the entropy weight method to select the Modbus RTU cross-TSN scheduling path; the fifth step is to use a two-dimensional packing algorithm to sort the boxes according to their width and height, and reasonably select the "available packing points" for packing to solve the heterogeneous network scheduling problem.

[0056] 2.2 Specific design of the scheme

[0057] 2.2.1 Establishment of Heterogeneous Scheduling Model

[0058] The first step to establish a heterogeneous network scheduling model is as follows: define a heterogeneous network N(L), which consists of multiple links l∈L={L TSN ,L ModRTU}, where l∈L TSN is a TSN link, l∈L ModRTU is a Modbus RTU link; the transmission flow in the heterogeneous network N(L) is defined as S = {S1, S2, ..., S i ,…S n}, n represents the total number of flows S transmitted in the heterogeneous network N(L);

[0059] Consider the end-to-end delay limit D S , bandwidth B S , transmission rate TR S and the transmission period T S , the transmission requirement R(S) of stream S is defined by the following set:

[0060] R(S)={I S ,P S ,T S ,DS ,B S ,TR S}(1)

[0061] Among them, I S Indicates the number of stream S, P S Indicates the priority of the stream;

[0062] 2.2.2Conversion of Modbus RTU data frames

[0063] The specific steps of Modbus RTU data frame conversion in the second step are: first, perform CRC check on the received Modbus RTU data frame. If the check result is a correct message, then extract the function code of the Modbus RTU protocol and remove the CRC check tail, add the MBAP message header, and form the ADU unit of the Modbus TCP protocol. Figure 2 shown.

[0064] 2.2.3 Solution of Data Frame Priority

[0065] Modbus TCP data frames can be divided into three categories according to data types, namely security, control, and monitoring, as shown below:

[0066] The first category is safety, such as emergency control, including emergency stop and automatic fire control, which is given the highest priority level 3;

[0067] The second category, control data streams such as frequency conversion control, open-loop control, and control with human participation in the loop are given medium priority 2;

[0068] The third category is monitoring and surveillance, with application scenarios including surveillance videos of production workshops, environmental testing, etc., and is given the lowest priority of 1.

[0069] In actual network environments, it may be necessary to consider various delays in the link, including the transmission delay at the sender. Propagation Delay Receiver store-and-forward delay The actual data frame sending and receiving time points need to be calculated, so that the end-to-end delay can be expressed as formula (2):

[0070]

[0071] The schematic diagram of data frame link transmission considering delay is as follows Figure 3 shown.

[0072] In the third step, the end-to-end delay requirements of the heterogeneous network can be divided into data flows whose delay requirements meet formula (3) as non-real-time data, data flows whose delay requirements meet formula (4) as real-time data, and data flows whose delay requirements meet formula (5) as time-critical data.

[0073] d i ≥100ms(3)

[0074] 1ms <d i <100ms(4)

[0075] d i <1ms(5)

[0076] The priority of the heterogeneous network can be solved by formula (5), where α and β are the coefficients of data type m and end-to-end delay n respectively, and α+β=1, and the scheduling priority reference coefficient δ i The results obtained by solving formula (6) are shown in Table 1. The scheduling priority reference coefficient δ i and scheduling priority P S The relationship is shown in Table 2.

[0077] δ i =αm+βn(6)

[0078] Table 1

[0079]

[0080]

[0081] Table 2

[0082] <![CDATA[Scheduling priority reference coefficient δ i > <![CDATA[Scheduling priority P S > <![CDATA[δ i >2.7]]> 7 <![CDATA[δ i >2.4]]> 6 <![CDATA[δ i >2.1]]> 5 <![CDATA[δ i >1.8]]> 4 <![CDATA[δ i >1.5]]> 3 <![CDATA[δ i >1.2]]> 2 <![CDATA[δ i >0.9]]> 1 <![CDATA[δ i <=0.9]]> 0

[0083] Combined with Table 1, the scheduling priority reference coefficient δ is obtained i and Table 2, the final Modbus RTU and TSN industrial heterogeneous network scheduling priorities are shown in Table 3.

[0084] Table 3

[0085] <![CDATA[Table 1 Reference coefficient δ of scheduling priority solved i > <![CDATA[Scheduling priority P S > 3 7 2.6 6 2.4 5 2.4 5 2 4 1.4 2 2.4 5 1.8 3 1 1

[0086] 2.2.4 AHP and entropy weight method to solve heterogeneous scheduling paths

[0087] After calculating the scheduling priority P of a data flow S After that, it is necessary to find a suitable path for the data flow. The network routing needs to consider constraints such as bandwidth and delay. The present invention considers the end-to-end delay upper limit D of the data flow. S , bandwidth B S and the transmission period T SThree evaluation indicators are used as path quality evaluation indicators, and the fuzzy hierarchical analysis method is used to determine the weight coefficient of the evaluation parameter. The entropy value of each path is calculated using the entropy weight method according to the weight coefficient, and a suitable path is selected after sorting according to the priority. The specific steps of the fourth step using the hierarchical analysis method and entropy weight method to select the Modbus RTU cross-TSN scheduling path are as follows:

[0088] Step 1: Select the upper limit of the end-to-end delay D of the data flow based on the selected available path S , bandwidth B S and the transmission period T S The three evaluation indicators are path quality evaluation indicators to select the appropriate path;

[0089] The upper limit of the end-to-end delay of the data flow D S , bandwidth B S The calculation of is shown in formula (6) (7):

[0090] B S =min{B Sl |l∈k}(6)

[0091]

[0092] In formula (7), m represents the number of links on path k.

[0093] The transmission period of data stream is T S , the calculation is shown in formula (8), lcm represents the least common multiple of the set, ensuring that each data stream is transmitted at least once in a transmission cycle, and the number of transmissions is the same in each transmission cycle. fi Represents the data flow f i transmission cycle.

[0094] T S = lcm(T f1 ,T f2 ,...T fi )(8)

[0095] Step 2: Use the analytic hierarchy process to determine the weight coefficients of the evaluation parameters;

[0096] Step 3: Calculate the entropy weight of each path using the entropy weight method according to the weight coefficient of the evaluation parameter, and then find the most suitable path based on the priority.

[0097] The specific steps of using the hierarchical analysis method are as follows:

[0098] Step 1: The normalized value of the jth evaluation index of the i-th path is X ij (0≤i≤n,0≤j≤3), i and j are both integers, rij The value of is determined by the importance of two evaluation indicators of the i-th path. The fuzzy judgment matrix representing the importance between two evaluation indicators of the i-th path can be expressed as shown in formula (9):

[0099]

[0100] Step 2: Perform row summation on the fuzzy judgment matrix to generate a one-dimensional vector Y:

[0101]

[0102] Step 3: Transform vector Y to generate the fuzzy consistency judgment matrix:

[0103]

[0104] Step 4: Fuzzy consistent judgment matrix Y ij Transform to generate a one-dimensional vector z, where z i Represents the importance of the jth indicator to the path:

[0105]

[0106] Step 5: Final calculation of the subjective weights of each indicator under the path:

[0107]

[0108] The jth indicator of the i-th path uses the nine-level standard method in Table 4 to judge the importance of the indicator.

[0109] Table 4

[0110] Scale definition Explanation 0.1 Absolutely not important Comparing the two parameters, parameter i is definitely less important than parameter j. 0.2 Very unimportant Comparing the two parameters, parameter i is much less important than parameter j. 0.3 Not important Comparing the two parameters, parameter i is less important than parameter j. 0.4 Slightly unimportant Comparing the two parameters, parameter i is slightly less important than parameter j. 0.5 Equally important Comparing the two parameters, parameter i is equally important than parameter j. 0.6 Slightly important Comparing the two parameters, parameter i is slightly more important than parameter j. 0.7 More important Comparing the two parameters, parameter i is more important than parameter j. 0.8 Very important Comparing the two parameters, parameter i is more important than parameter j. 0.9 Absolutely important Comparing the two parameters, parameter i is definitely more important than parameter j.

[0111] The specific steps of calculating the entropy weight of each path using the entropy weight method based on the weight coefficient of the evaluation parameter of each path calculated by formula (13) to find the most suitable path are:

[0112] Step 1: Calculate the entropy value of each evaluation index. The entropy value calculation formula for the jth evaluation index is:

[0113]

[0114] Where P ij is the ratio of the score of the i-th path on the j-th evaluation index to the score of all paths on this index.

[0115] Step 2: Based on the end-to-end delay limit D of the previously selected data stream S , bandwidth B S and the transmission period T SThe weights of the three evaluation indicators are w1, w2, and w3, which are used to adjust the importance of path evaluation. The entropy weight calculation formula of the jth indicator is:

[0116]

[0117] In the formula, (1-e j ) is the discrete degree of the jth evaluation index. Since when the values ​​of each path on the evaluation index j are exactly the same, the entropy value of the evaluation index e j Reaching the maximum value does not provide valuable information for path selection, that is, under this evaluation indicator, all paths are indifferent, and the entropy weight of this evaluation indicator is the smallest.

[0118] Step 3: Comprehensive score of each path. The formula for calculating the comprehensive score of the ith path is:

[0119]

[0120] In this way, the available paths under a certain data flow can be selected for scheduling based on the obtained comprehensive scores of the paths.

[0121] 2.2.5 Bin Packing Algorithm for Solving Heterogeneous Network Scheduling Problems

[0122] The essence of heterogeneous network joint scheduling is to arrange data flows in heterogeneous network links according to scheduling requirements, so that heterogeneous networks can be scheduled according to transmission requirements, improve the utilization efficiency of network resources, reduce latency, etc. A key issue in the scheduling problem is the generation of the scheduling table. The collaborative scheduling subsystem generates a scheduling table after calculation by the packing algorithm and sends it to the data plane through the CNC configuration interface. The data plane network schedules traffic according to the scheduling table.

[0123] The bin packing algorithm is a classic optimization problem algorithm, which is mainly used in situations where multiple items (or tasks) need to be placed into a limited number of containers (or resources) as efficiently as possible. This problem usually involves goals such as minimizing the number of containers used or maximizing container utilization. It is widely used in planning and scheduling, resource allocation, such as CPU task scheduling, memory management, etc. Some classic algorithms of the bin packing problem can be used to design and implement traffic scheduling in heterogeneous networks.

[0124] According to the previously selected path, the bin packing algorithm is used to solve the scheduling problem. The two-dimensional bin packing algorithm abstracts the link time resources into "empty boxes" and the time-triggered communication tasks into "two-dimensional objects". The width of the box is related to the transmission rate of the link. Figure 4 This is a diagram showing the relationship between the data stream transmission cycle and the basic cycle.

[0125] Each data stream has a period. When different data streams need to be transmitted in a link, a basic period T can be defined. GCD is the greatest common divisor of all data stream periods, which ensures that each data stream is transmitted at most once within the basic period. The definition of the data stream transmission period is shown in formula (8). The relationship between the data stream transmission period and the basic period can be seen in Figure 4 .

[0126] Each data stream can be regarded as a "two-dimensional object" with width and height. The width W and height H of the "two-dimensional object" can be expressed as formulas (19) and (20). The width W is equal to the basic period, and the height H is the superposition of each basic period.

[0127]

[0128] W=T GCD (20)

[0129] The specific process of packing is as follows: if the box is empty, there is only one packing point P0 (0, 0). When loading an item, you can only choose the existing packing point to place it. When the first item is placed, the "available packing point" P0 will be consumed, and two new packing points P1 (0, item 1 height) and P2 (item 1 width, 0) will be generated. When the second item is placed, only these two packing points can be selected, and new packing points can also be calculated. When the width or height of the later placed item is the same as the previous item, only one "available packing point" will be consumed and a new "available packing point" will be generated. The specific process is as follows: Figure 5 and Figure 6 shown.

[0130] To simplify the generation of "available packing points", you can first sort the items by width and height, so that each time an item is placed, only one "available packing point" is consumed and at most two "available packing points" are generated. The specific steps of the packing algorithm are as follows:

[0131] Step 1: Initialize the queue of items to be packed, sort them by height and width, and correspond the height to the height of each basic cycle;

[0132] Step 2: Initialize the "available packing point" queue. The initial queue is empty. First, place the lower left corner of the first item on the only "available packing point" at this time. At this time, two new "available packing points" are generated;

[0133] Step 3: Update the "available packing point" queue and the item queue, and determine whether the item queue is empty. If it is not empty, loop to put items into the "available packing point". If it is empty, save the item packing result and the "available packing point" queue, which also means that all items are put into the box, indicating that the traffic information can be scheduled and generate a heterogeneous network scheduling table. If there are still items to be packed at the end of the loop, it means that it cannot be scheduled. The flowchart of the packing algorithm is as follows Figure 7 shown.

[0134] After the scheduling table is generated, it is sent to the heterogeneous network on the data plane through the CNC configuration interface. The data flow on the data plane is sent from the Modbus RTU master on the left to the Modbus RTU slave through the TSN network according to this scheduling table.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A Modbus RTU cross-TSN scheduling method based on entropy weight method and box packing algorithm, characterized by: The following steps are involved: Step 1: Establish a heterogeneous network scheduling model, including Time-Sensitive Networking (TSN) links and Modbus Remote Terminal Unit (Modbus RTU) links, as well as the transmission flows; Step 2: Convert the Modbus RTU data frame into a Modbus Transmission Control Protocol (Modbus TCP) data frame; Step 3: Divide the priority of ModbusRTU cross-TSN data frames according to the data type of Modbus TCP data frames and the end-to-end delay requirements of heterogeneous networks; Step 4: Use the hierarchical analysis method and entropy weight method to select the Modbus RTU cross-TSN scheduling path, and use the end-to-end delay, bandwidth and transmission cycle as evaluation indicators for path selection; Step 5: Use the two-dimensional bin packing algorithm to perform scheduling calculations on heterogeneous networks, generate a heterogeneous network scheduling table, and guide the transmission of data flows.

2. The Modbus RTU cross-TSN scheduling method based on entropy weight method and box packing algorithm according to claim 1 is characterized in that: In step 2, the step of converting the Modbus RTU data frame into the Modbus TCP data frame includes: Perform CRC check on the received Modbus RTU data frame; Extract the function code of the Modbus RTU protocol and remove the CRC check tail; Add the MBAP message header to form the ADU unit of the Modbus TCP protocol.

3. The Modbus RTU cross-TSN scheduling method based on entropy weight method and box packing algorithm according to claim 2 is characterized in that: In step 3, the step of dividing the priority of Modbus RTU cross-TSN data frames includes: Modbus TCP data frames are divided into three levels according to data type: security, control and monitoring; According to the end-to-end delay requirement of data frames, data frames are divided into real-time data and non-real-time data; The scheduling priority of the data frame is calculated based on the weight coefficient of the data type and the end-to-end delay.

4. The Modbus RTU cross-TSN scheduling method based on entropy weight method and box packing algorithm according to claim 3 is characterized in that: In step 4, the steps of selecting the Modbus RTU cross-TSN scheduling path using the hierarchical analysis method and the entropy weight method include: The analytic hierarchy process is used to determine the importance of end-to-end delay, bandwidth and transmission period to the path; The entropy weight method is used to calculate the entropy weight of each path, and the most appropriate path is selected based on the entropy weight and scheduling priority.

5. The Modbus RTU cross-TSN scheduling method based on entropy weight method and box packing algorithm according to claim 4 is characterized in that: In step 5, the step of using a two-dimensional bin packing algorithm to perform scheduling calculations on heterogeneous networks includes: Abstract link time resources as "empty boxes" and time-triggered communication tasks as "two-dimensional objects"; Sort the items by height and width, and put them into the "empty box" one by one; Update the "available packing points" queue and the item queue until all items are placed in the "box" or no more items can be placed.

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