TSN-based industrial equipment collaborative control method, system and storage medium

Abstract

The present invention relates to the field of industrial control technology, and in particular to a TSN-based industrial equipment collaborative control method, system, and readable storage medium. The method comprises the following steps: a switch obtains the TSN network from a preset start detection time t1 to the current time t K The present invention adopts a static traffic scheduling strategy to ensure that critical control traffic is transmitted first; it uses an intelligent traffic dynamic scheduling algorithm, combined with the ant colony optimization algorithm, to analyze network status in real time and optimize network resource allocation. This effectively solves the problem that traditional traffic scheduling cannot adapt to network changes in real time.

Description

Technical Field

[0001] The present invention relates to the field of industrial control technology, and in particular to a TSN-based industrial equipment collaborative control method, system, and readable storage medium. Background Art

[0002] In existing industrial automation systems, communication between devices typically relies on traditional Ethernet, fieldbus, and industrial wireless networks, which suffer from high latency, poor reliability, and an inability to meet real-time requirements. In the industrial network market, traditional Ethernet continues to dominate due to its faster speed and more flexible topology.

[0003] In extreme situations such as network congestion, hardware failures, or configuration errors, the latency of traditional Ethernet can increase significantly and may even approach infinity. Time-Sensitive Networking (TSN), an emerging network technology, can provide deterministic communication services for industrial equipment, precisely controlling latency, jitter, packet loss, bandwidth, and data transmission paths, addressing the shortcomings of traditional Ethernet in real-time communication. By optimizing time synchronization, traffic scheduling, and interoperability, TSN enables the mixed transport of periodic and aperiodic data within the same network, reducing the complexity of the entire communication network. Furthermore, TSN's open and compatible technical characteristics can break the closed nature of traditional industrial Ethernet protocols, enabling information exchange and functional collaboration between different devices, providing important support for the development of more efficient and reliable industrial networks.

[0004] However, TSN technology still faces the following challenges when integrating with existing industrial automation systems:

[0005] First, in modern industrial environments, network connections and data traffic between devices are becoming increasingly complex. Traditional "two-tier, three-level" network architectures often struggle to handle the massive number of devices connecting and transmitting data, resulting in difficult network management and low data transmission efficiency.

[0006] Second, current traffic scheduling methods mostly rely on static priorities and fixed time slot allocation strategies, lacking the flexibility to adapt to different types of traffic. For some critical control traffic, traditional scheduling methods cannot ensure its priority transmission, resulting in transmission delays for control commands with high real-time requirements, affecting the effectiveness of collaborative control of industrial equipment.

[0007] Third, although TSN has certain advantages in time synchronization and traffic scheduling, in complex industrial environments, the operating status of equipment and network conditions are dynamic. Traditional network management methods cannot adjust traffic priorities and network resource allocation in real time, resulting in network congestion, delays, and data loss.

[0008] In view of this, it is necessary to propose a new collaborative control method for industrial equipment based on TSN. Summary of the Invention

[0009] The main purpose of the present invention is to provide a TSN-based industrial equipment collaborative control method, system and readable storage medium to solve the above technical problems.

[0010] To achieve the above objectives, the present invention provides a TSN-based industrial equipment collaborative control method, comprising the following steps:

[0011] S1, the switch obtains the TSN network from the preset start detection time To the current moment of traffic, and The traffic obtained at any moment is evenly divided into N traffic As of now , the set of each flow is:

[0012] ;

[0013] S21, construct the optimization objective function of the static scheduling strategy:

[0014] ,

[0015] Where N represents the total number of flows, iϵ[1,N], represents the delay of the i-th flow, represents the jitter of the i-th flow, represents the packet loss rate of the i-th flow, 、 、 are the weight coefficients of the delay, jitter and packet loss rate of the i-th flow respectively;

[0016] S22, obtaining the traffic priority, allocated time slot and bandwidth resources that minimize the optimization objective function in step S21;

[0017] S3 builds a dynamic scheduling model to obtain adjusted traffic priorities, allocated time slots, and bandwidth resources. Specifically, it includes:

[0018] S31, the delay of the i-th flow , the jitter of the i-th flow , the packet loss rate of the i-th flow , perform standardization processing, randomly generate candidate scheduling solutions to obtain an ant colony containing multiple candidate scheduling solutions;

[0019] S321, calculate the scheduling decision transfer path length of each candidate scheduling solution ,

[0020] ;

[0021] in, is the traffic transmission delay of the kth ant, is the packet loss rate, is the jitter value, It is the influencing factor of the equipment status;

[0022] S322, calculate the increased pheromone concentration of each path , ; Where M is the number of ants in the ant colony, Q is a constant, is the path length of the kth ant;

[0023] S331, according to the pheromone concentration obtained in step S322 , the heuristic information guides the movement of ants and obtains the probability of scheduling decision transfer ,

[0024] ;

[0025] in, is the set of nodes that ant k is allowed to choose, For inspirational information, and are the importance parameters of pheromone and heuristic information respectively;

[0026] S332, update pheromone concentration ,

[0027] ;

[0028] in, represents the pheromone concentration from node i to node j at time t, is the pheromone volatility coefficient, is the newly added pheromone concentration;

[0029] S34; Calculate the fitness of each candidate scheduling scheme, select an excellent scheduling scheme according to the fitness value, and perform pheromone update and ant colony movement until the maximum number of iterations is reached;

[0030] S35, obtaining an optimal scheduling solution with a maximum fitness value to obtain optimal traffic priority allocation, time slot allocation, and bandwidth resource allocation results that adapt to the current network situation.

[0031] Preferably, in step S2,

[0032] ,

[0033] ,

[0034] ;

[0035] in, 、 、 are the delay weight, jitter weight, and packet loss rate weight of the i-th flow, respectively. Their values ​​are all between 0 and 1, and their sum is 1; 、 、 are the maximum allowed delay, maximum jitter and maximum packet loss rate of the i-th flow respectively.

[0036] Preferably, according to the i-th flow The delay, jitter, and packet loss rate are calculated as of the current moment. The probability ratio of delay, jitter, and packet loss rate for the i-th flow is used as the delay weight, jitter weight, and packet loss rate weight of the i-th flow.

[0037] Preferably, the step S2 further includes the step of: N flows The bandwidth and number of time slots are summed up to obtain the total bandwidth and total number of time slots of the TSN network.

[0038] Preferably, step S2 further includes constructing the constraint conditions of the optimization objective function of the static scheduling strategy according to the total bandwidth of the TSN network and the total number of network time slots:

[0039] ,

[0040] ;

[0041] in, represents the bandwidth requirement of the i-th flow, is the total bandwidth of the TSN network, represents the transmission time slot requirement of the i-th flow, is the total number of time slots in the TSN network.

[0042] Preferably, the step S34 includes:

[0043] S341, according to the formula:

[0044] ,

[0045] Calculate the fitness of each candidate scheduling solution ;in, 、 、 、 These are the weight coefficients of the factors affecting latency, jitter, packet loss rate, and device status;

[0046] S342, obtain the scheduling plan with the highest fitness value, perform pheromone update and ant colony movement until the maximum number of iterations is reached.

[0047] Preferably, the step S1 is preceded by the step of: constructing a hierarchical network topology in a TSN network including switches and servers; wherein the hierarchical network topology includes a factory level responsible for global management and data processing and a device level responsible for device control and data acquisition.

[0048] The present invention also provides a TSN-based industrial equipment collaborative control system, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the TSN-based industrial equipment collaborative control method as described in any one of the above items are implemented.

[0049] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the TSN-based industrial equipment collaborative control method as described in any one of the above items.

[0050] In response to the shortcomings and improvement needs of existing technologies, the present invention provides a TSN-based industrial equipment collaborative control method, which aims to solve the problems of poor real-time performance and inflexible traffic scheduling in existing industrial networks in equipment collaborative control. Overall, the technical solution conceived by the present invention can achieve the following beneficial effects compared with existing technologies:

[0051] The present invention adopts a static traffic scheduling strategy. By setting different priorities and time slot allocation rules for different types of traffic, it ensures that key control traffic can be transmitted first, thereby improving the real-time control capability of industrial equipment.

[0052] The present invention adopts an intelligent dynamic traffic scheduling algorithm, combined with an ant colony optimization algorithm to analyze network status in real time, and dynamically adjusts traffic priority according to device operating status and communication needs, thereby optimizing network resource allocation; it effectively solves the problem that traditional traffic scheduling cannot adapt to network changes in real time, and improves network flexibility and resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings, as part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention but do not constitute undue limitations thereon. Obviously, the drawings described below are merely examples, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0054] Figure 1 The figure is a flowchart of a TSN-based industrial equipment collaborative control method in one embodiment of the present invention.

[0055] Figure 2 This is an example diagram of the TSN network architecture according to an embodiment of the present invention.

[0056] Figure 3 for Figure 1 Flowchart of sub-steps of step S3 in FIG.

[0057] Figure 4 The figure is a schematic diagram of the hardware structure for running the TSN-based industrial equipment collaborative control method in one embodiment of the present invention.

[0058] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0059] The following is a clear and complete description of the technical problems solved by the embodiments of the present invention, the technical solutions adopted, and the technical effects achieved, in conjunction with the accompanying drawings and specific embodiments. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other equivalent or obviously modified embodiments obtained by ordinary technicians in this field without paying creative work fall within the scope of protection of the present invention. The embodiments of the present invention can be concretized in a variety of different ways as defined and covered in the claims.

[0060] It should be noted that in the following description, many specific details are given for ease of understanding, but it is obvious that the present invention can be implemented without these specific details.

[0061] It should be noted that, in the absence of clear limitations or conflicts, the various embodiments of the present invention and the technical features therein can be combined with each other to form a technical solution.

[0062] The basic concept of this invention is to construct a collaborative control method for industrial equipment based on time-sensitive networking (TSN). This method leverages the transmission characteristics of TSN to achieve precise collaborative control between industrial devices. First, a TSN network architecture for industrial equipment is established. Then, the static traffic scheduling strategy within the TSN network is optimized to ensure that critical control instructions and data are transmitted first. Finally, an intelligent traffic scheduling algorithm is implemented to dynamically adjust traffic priorities based on network status and device requirements, further improving overall system performance.

[0063] The present invention provides a TSN network-based industrial equipment collaborative control method, which is applied to a TSN network including switches and servers.

[0064] like Figure 2 As shown, the TSN network of the present invention adopts a "two-layer, two-level" factory intranet architecture, where the two layers refer to the Information Technology (IT) layer and the Operational Technology (OT) layer; the two levels refer to the factory level and the device level. The factory level is responsible for global management and data processing, and servers and storage devices are connected to network switches through industrial Ethernet to achieve global management and data processing; the device level is responsible for direct control of equipment and data collection, and industrial equipment such as sensors, actuators, and Programmable Logic Controllers (PLCs) in each workshop are connected to network switches through industrial Ethernet to achieve real-time data transmission.

[0065] Please refer to Figure 1 and Figure 3 , the present invention proposes a TSN-based industrial equipment collaborative control method, comprising the steps of:

[0066] S1, the switch obtains the TSN network from the preset start detection time To the current moment of traffic, and The traffic obtained at any moment is evenly divided into N traffic As of now , the set of each flow is:

[0067] .

[0068] S21, construct the optimization objective function of the static scheduling strategy:

[0069] ,

[0070] Where N represents the total number of flows, iϵ[1,N], represents the delay of the i-th flow, represents the jitter of the i-th flow, represents the packet loss rate of the i-th flow, 、 、 are the weight coefficients of the delay, jitter and packet loss rate of the i-th flow respectively;

[0071] S22, obtaining the traffic priority, allocated time slot and bandwidth resources that minimize the optimization objective function in step S21.

[0072] Based on step S1, the switch obtains the current time obtained in step S1. The i-th flow The delay, jitter, and packet loss rate are sent to the server, and the obtained data are used as of the current time. The maximum allowed delay, maximum jitter, and maximum packet loss rate of the i-th flow are obtained from the delay, jitter, and packet loss rate of the i-th flow.

[0073] Preferably, in step S2,

[0074] ,

[0075] ,

[0076] ;

[0077] in, 、 、 are the delay weight, jitter weight, and packet loss rate weight of the i-th flow, respectively. Their values ​​are all between 0 and 1, and their sum is 1; 、 、 are the maximum allowed delay, maximum jitter and maximum packet loss rate of the i-th flow respectively.

[0078] Preferably, according to the i-th flow The delay, jitter, and packet loss rate are calculated as of the current moment. The probability ratio of delay, jitter, and packet loss rate for the i-th flow is used as the delay weight, jitter weight, and packet loss rate weight of the i-th flow.

[0079] Preferably, the step S2 further includes the step of: N flows The bandwidth and number of time slots are summed up to obtain the total bandwidth and total number of time slots of the TSN network.

[0080] Preferably, step S2 further includes constructing the constraint conditions of the optimization objective function of the static scheduling strategy according to the total bandwidth of the TSN network and the total number of network time slots:

[0081] ,

[0082] ;

[0083] in, represents the bandwidth requirement of the i-th flow, is the total bandwidth of the TSN network, represents the transmission time slot requirement of the i-th flow, is the total number of time slots in the TSN network.

[0084] With the support of step S1, traffic transmission data such as packet size, transmission delay, packet loss rate, etc. are collected, and the operating status data of the device such as temperature, humidity, vibration, etc. are obtained. Then, an intelligent scheduling algorithm based on the ant colony optimization algorithm is proposed to build a dynamic scheduling model, modeling the traffic scheduling problem as an optimization problem, with the goal of optimizing the total time and communication overhead of traffic transmission while meeting the deadline.

[0085] S3 builds a dynamic scheduling model to obtain adjusted traffic priorities, allocated time slots, and bandwidth resources. Specifically, it includes:

[0086] S3 builds a dynamic scheduling model to obtain adjusted traffic priorities, allocated time slots, and bandwidth resources. Specifically, it includes:

[0087] S31, the delay of the i-th flow , the jitter of the i-th flow , the packet loss rate of the i-th flow , perform standardization processing, randomly generate candidate scheduling solutions to obtain an ant colony containing multiple candidate scheduling solutions;

[0088] S321, calculate the scheduling decision transfer path length of each candidate scheduling solution ,

[0089] ;

[0090] in, is the traffic transmission delay of the kth ant, is the packet loss rate, is the jitter value, It is the influencing factor of the equipment status;

[0091] S322, calculate the increased pheromone concentration of each path , ; Where M is the number of ants in the ant colony, Q is a constant, is the path length of the kth ant;

[0092] S331, according to the pheromone concentration obtained in step S322 , the heuristic information guides the movement of ants and obtains the probability of scheduling decision transfer ,

[0093] ;

[0094] in, is the set of nodes that ant k is allowed to choose, For inspirational information, and are the importance parameters of pheromone and heuristic information respectively;

[0095] S332, update pheromone concentration ,

[0096] ;

[0097] in, represents the pheromone concentration from node i to node j at time t, is the pheromone volatility coefficient, is the newly added pheromone concentration;

[0098] S34; Calculate the fitness of each candidate scheduling scheme, select an excellent scheduling scheme according to the fitness value, and perform pheromone update and ant colony movement until the maximum number of iterations is reached;

[0099] S35, obtaining an optimal scheduling solution with a maximum fitness value to obtain optimal traffic priority allocation, time slot allocation, and bandwidth resource allocation results that adapt to the current network situation.

[0100] Preferably, the step S34 includes:

[0101] S341, according to the formula:

[0102] ,

[0103] Calculate the fitness of each candidate scheduling solution ;in, 、 、 、 These are the weight coefficients of the factors affecting latency, jitter, packet loss rate, and device status;

[0104] S342, obtain the scheduling plan with the highest fitness value, perform pheromone update and ant colony movement until the maximum number of iterations is reached.

[0105] Preferably, the step S1 is preceded by the step of: constructing a hierarchical network topology in a TSN network including switches and servers; wherein the hierarchical network topology includes a factory level responsible for global management and data processing and a device level responsible for device control and data acquisition.

[0106] The present invention also provides a TSN-based industrial equipment collaborative control system, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the TSN-based industrial equipment collaborative control method as described in any one of the above items are implemented.

[0107] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the TSN-based industrial equipment collaborative control method as described in any one of the above items.

[0108] In response to the shortcomings and improvement needs of existing technologies, the present invention provides a TSN-based industrial equipment collaborative control method, which aims to solve the problems of poor real-time performance and inflexible traffic scheduling in existing industrial networks in equipment collaborative control. Overall, the technical solution conceived by the present invention can achieve the following beneficial effects compared with existing technologies:

[0109] The present invention adopts a static traffic scheduling strategy. By setting different priorities and time slot allocation rules for different types of traffic, it ensures that key control traffic can be transmitted first, thereby improving the real-time control capability of industrial equipment.

[0110] The present invention adopts an intelligent dynamic traffic scheduling algorithm, combined with an ant colony optimization algorithm to analyze network status in real time, and dynamically adjusts traffic priority according to device operating status and communication needs, thereby optimizing network resource allocation; it effectively solves the problem that traditional traffic scheduling cannot adapt to network changes in real time, and improves network flexibility and resource utilization efficiency.

[0111] Figure 4 FIG is a schematic diagram of the hardware structure for running the TSN-based industrial equipment collaborative control method provided by an embodiment of the present invention. Figure 4 As shown, this embodiment / computer 6 includes: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60, such as a program for executing a TSN-based industrial equipment collaborative control method. When the processor 60 executes the computer program 62, the steps of the aforementioned method embodiments are implemented. Alternatively, when the processor 60 executes the computer program 62, the functions of the modules / units in the aforementioned device embodiments are implemented.

[0112] For example, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 62 in the computer 6.

[0113] The computer 6 can be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer 6 device can include, but is not limited to, a processor 60 and a memory 61. It can be understood by those skilled in the art that Figure 4 This is only an example of computer 6 and does not constitute a limitation on computer 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer 6 may also include input and output devices, network access devices, buses, etc.

[0114] The processor 60 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0115] The memory 61 can be an internal storage unit of the computer 6, such as the hard drive or memory of the computer 6. Alternatively, the memory 61 can be an external storage device of the computer 6, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, or a Flash Card (FC). Furthermore, the memory 61 can include both the internal storage unit of the computer 6 and an external storage device. The memory 61 is used to store the computer program 62 and other programs and data required by the computer 6. The memory 61 can also be used to temporarily store data that has been output or is about to be output.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0117] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0118] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0119] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0120] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0121] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0122] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0123] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A TSN-based industrial equipment collaborative control method, characterized in that: Including steps: S1, the switch obtains the TSN network from the preset start detection time To the current moment of traffic, and The traffic obtained at any moment is evenly divided into N traffic , as of the current moment , the set of each flow is: ； S21, construct the optimization objective function of the static scheduling strategy: ， Where N represents the total number of flows, iϵ[1,N], represents the delay of the i-th flow, represents the jitter of the i-th flow, represents the packet loss rate of the i-th flow, 、 、 are the weight coefficients of the delay, jitter and packet loss rate of the i-th flow respectively; among them, , , ;in, 、 、 are the delay weight, jitter weight, and packet loss rate weight of the i-th flow, respectively. Their values ​​are all between 0 and 1, and their sum is 1; 、 、 are the maximum allowed delay, maximum jitter, and maximum packet loss rate of the i-th flow respectively; S22, obtaining the traffic priority, allocated time slot and bandwidth resources that minimize the optimization objective function in step S21; said step S22 includes the steps of: according to the current moment N flows The bandwidth and number of time slots are summed up to obtain the total bandwidth and total number of time slots of the TSN network; and the constraint conditions of the optimization objective function of the static scheduling strategy are constructed according to the total bandwidth and total number of time slots of the TSN network: , ;in, represents the bandwidth requirement of the i-th flow, is the total bandwidth of the TSN network, represents the transmission time slot requirement of the i-th flow, is the total number of time slots in the TSN network; S3 builds a dynamic scheduling model to obtain adjusted traffic priorities, allocated time slots, and bandwidth resources. Specifically, it includes: S31, the delay of the i-th flow , the jitter of the i-th flow , the packet loss rate of the i-th flow , perform standardization processing, randomly generate candidate scheduling solutions to obtain an ant colony containing multiple candidate scheduling solutions; S321, calculate the scheduling decision transfer path length of each candidate scheduling solution , ； in, is the traffic transmission delay of the kth ant, is the packet loss rate, is the jitter value, It is the factor affecting the equipment status; S322, calculate the pheromone concentration added to each path , ; Where M is the number of ants in the ant colony, Q is a constant, The length of the scheduling decision transfer path for each candidate scheduling solution; S331, according to the pheromone concentration obtained in step S322 , the heuristic information guides the movement of ants and obtains the probability of scheduling decision transfer , ； in, is the set of nodes that ant k is allowed to choose, For inspirational information, and are the importance parameters of pheromone and heuristic information respectively; S332, update pheromone concentration , ； in, represents the pheromone concentration from node i to node j at time t, is the pheromone volatility coefficient, Increased pheromone concentration for each path segment; S34; Calculate the fitness of each candidate scheduling scheme, select an excellent scheduling scheme according to the fitness value, and perform pheromone update and ant colony movement until the maximum number of iterations is reached; S35, obtaining an optimal scheduling solution with a maximum fitness value to obtain optimal traffic priority allocation, time slot allocation, and bandwidth resource allocation results that adapt to the current network situation.

2. The TSN-based industrial equipment collaborative control method according to claim 1, characterized in that: According to the i-th flow The delay, jitter, and packet loss rate are calculated as of the current moment. The probability ratio of delay, jitter, and packet loss rate for the i-th flow is used as the delay weight, jitter weight, and packet loss rate weight of the i-th flow.

3. The TSN-based industrial equipment collaborative control method according to claim 1, characterized in that: The step S34 includes: S341, according to the formula: ， Calculate the fitness of each candidate scheduling solution ;in, 、 、 、 These are the weight coefficients of the factors affecting latency, jitter, packet loss rate, and device status; S342, obtain the scheduling plan with the highest fitness value, perform pheromone update and ant colony movement until the maximum number of iterations is reached.

4. The TSN-based industrial equipment collaborative control method according to claim 1, characterized in that: The step S1 may also include the following steps: constructing a hierarchical network topology in a TSN network including switches and servers; wherein the hierarchical network topology includes a factory level responsible for global management and data processing and a device level responsible for device control and data acquisition.

5. A TSN-based industrial equipment collaborative control system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the TSN-based industrial equipment collaborative control method according to any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the TSN-based industrial equipment collaborative control method according to any one of claims 1 to 4 are implemented.

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