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

Abstract

The invention relates to the technical field of industrial control, in particular to a TSN-based industrial equipment cooperative control method and system and a readable storage medium. The method comprises the following steps that: a switch acquires the flow of a TSN network from a preset detection starting moment t1 to a current moment tK; constructing an optimization objective function of the static scheduling strategy; obtaining a flow priority, a distribution time slot and a bandwidth resource which enable the optimization objective function to reach the minimum; and constructing a dynamic scheduling model based on an intelligent scheduling algorithm of an ant colony optimization algorithm so as to obtain adjusted flow priority, allocation time slot and bandwidth resources. According to the invention, a static traffic scheduling strategy is adopted to ensure that key control traffic can be transmitted preferentially; the network state is analyzed in real time by adopting an intelligent flow dynamic scheduling algorithm in combination with an ant colony optimization algorithm, and network resource allocation is optimized; the problem that traditional flow scheduling cannot adapt to network changes in real time is effectively solved.

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 usually relies on traditional Ethernet, fieldbus and industrial wireless networks, which have problems such as high latency, poor reliability and 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 cases such as network congestion, hardware failure or configuration errors, the latency of traditional Ethernet will increase abnormally and significantly, and may even tend to infinity. As an emerging network technology, Time-Sensitive Network (TSN) can provide deterministic communication services for industrial equipment, accurately control latency, jitter, packet loss rate, bandwidth and data transmission path, and solve the shortcomings of traditional Ethernet in real-time communication. It optimizes time synchronization, traffic scheduling and interoperability to achieve mixed carrying of periodic data and non-periodic data in the same network, reducing the complexity of the entire communication network. At the same time, TSN also has the technical characteristics of openness and compatibility, which can break the closedness between traditional industrial Ethernet protocols, realize information exchange and functional collaboration between different devices, and provide important support for the development of industrial networks in a more efficient and reliable direction.

[0004] However, TSN technology still faces the following challenges in the process of integration with existing industrial automation systems: First, in modern industrial environments, network connections and data traffic between devices are becoming increasingly complex. The traditional "two-layer, three-level" network architecture often cannot cope with the access and data transmission of a large number of devices, resulting in difficult network management and low data transmission efficiency.

[0005] Second, most current traffic scheduling methods use static priority and fixed time slot allocation strategies, which lack the ability to flexibly adapt to different types of traffic. For some critical control traffic, traditional scheduling methods cannot ensure its priority transmission, causing transmission delays for control instructions with high real-time requirements, affecting the effect of collaborative control of industrial equipment.

[0006] 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 dynamically changing. Traditional network management methods cannot adjust traffic priorities and network resource allocation in real time, resulting in network congestion, delays, and data loss.

[0007] In view of this, it is necessary to propose a new TSN-based collaborative control method for industrial equipment. Summary of the invention

[0008] 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-mentioned technical problems.

[0009] To achieve the above object, the present invention provides a TSN-based industrial equipment collaborative control method, comprising the steps of: S1, the switch obtains the TSN network from the preset start detection time To the current time of traffic, and The traffic obtained at any time is evenly divided into N traffic As of now , 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 ith flow, represents the jitter of the ith 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; S22, obtaining the traffic priority, allocated time slot and bandwidth resources that minimize the optimization objective function in step S21; S3, build a dynamic scheduling model to obtain adjusted traffic priority, allocated time slots and bandwidth resources, including: 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, 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 influencing factor of the equipment status; 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; S331, according to the pheromone concentration obtained in step S322 , the inspiration 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, is the newly added pheromone concentration; 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 are adapted to the current network conditions.

[0010] Preferably, in step S2, , , ; 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 ith flow respectively.

[0011] Preferably, according to the i-th flow The latency, jitter, and packet loss rate of the data are counted up to the current time. 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.

[0012] 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.

[0013] Preferably, the step S2 further includes constructing the constraint condition 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: , ; in, represents the bandwidth requirement of the ith 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.

[0014] Preferably, the step S34 includes: S341, according to the formula: , Calculate the fitness of each candidate scheduling solution ;in, , , , 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.

[0015] Preferably, the step S1 also includes 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.

[0016] The present invention also provides 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, wherein 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.

[0017] 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.

[0018] In view of the deficiencies and improvement needs in the prior art, the TSN-based industrial equipment collaborative control method provided by the present invention aims to solve the problems of poor real-time performance and inflexible traffic scheduling in the equipment collaborative control of the existing industrial network. In general, the technical solution conceived by the present invention can achieve the following beneficial effects compared with the prior art: The present invention adopts a static traffic scheduling strategy, which ensures that key control traffic can be transmitted first by setting different priorities and time slot allocation rules for different types of traffic, thereby improving the real-time control capability of industrial equipment. The present invention adopts an intelligent traffic dynamic 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

[0019] The accompanying drawings are part of the present invention and are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but do not constitute an improper limitation of the present invention. Obviously, the accompanying drawings described below are only some embodiments. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0020] Figure 1 The figure is a flow chart of a TSN-based industrial equipment collaborative control method in one embodiment of the present invention.

[0021] Figure 2 This is an example diagram of a TSN network architecture according to an embodiment of the present invention.

[0022] Figure 3 for Figure 1 Flowchart of sub-steps of step S3 in FIG.

[0023] 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.

[0024] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] 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 described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other equivalent or obviously modified embodiments obtained by ordinary technicians in this field without 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.

[0026] 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.

[0027] It should be noted that, in the absence of clear limitation or conflict, the various embodiments of the present invention and the technical features therein can be combined with each other to form a technical solution.

[0028] The basic idea of ​​the present invention is to construct a collaborative control method for industrial equipment based on time-sensitive network (TSN), and use the transmission characteristics of TSN to achieve precise collaborative control between industrial equipment. First, build the TSN network architecture for industrial equipment. Then, optimize the static traffic scheduling strategy in the TSN network to ensure that key control instructions and data can be transmitted first. Finally, use an intelligent traffic scheduling algorithm to dynamically adjust the traffic priority according to the network status and equipment requirements to further improve the overall performance of the system.

[0029] The present invention provides a TSN network-based industrial equipment collaborative control method, which is applied to a TSN network including a switch and a server.

[0030] like Figure 2 As shown, the TSN network of the present invention adopts a "two-layer and two-level" factory intranet architecture, wherein the two layers refer to the information technology (IT) layer and the operation technology (OT) layer; the two levels refer to the factory level and the equipment level, wherein the factory level is responsible for global management and data processing, and the server and storage device are connected to the network switch through the industrial Ethernet to realize global management and data processing; the equipment level is responsible for the direct control and data collection of the equipment, and the sensors, actuators, programmable logic controllers (PLC) and other industrial equipment in each workshop are connected to the network switch through the industrial Ethernet to realize real-time data transmission.

[0031] Please refer to Figure 1 and Figure 3The present invention proposes a TSN-based industrial equipment collaborative control method, comprising the steps of: S1, the switch obtains the TSN network from the preset start detection time To the current time of traffic, and The traffic obtained at any time is evenly divided into N traffic As of now , the set of each flow is: .

[0032] 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 ith flow, represents the jitter of the ith 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; S22, obtaining the traffic priority, allocated time slot and bandwidth resources that minimize the optimization objective function in step S21.

[0033] 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 is used as of the current time. The maximum allowed delay, maximum jitter, and maximum packet loss rate of the ith flow are obtained from the delay, jitter, and packet loss rate of the ith flow.

[0034] Preferably, in step S2, , , ; 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 ith flow respectively.

[0035] Preferably, according to the i-th flow The latency, jitter, and packet loss rate of the data are counted up to the current time. 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.

[0036] 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.

[0037] Preferably, the step S2 further includes constructing the constraint condition 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: , ; in, represents the bandwidth requirement of the ith 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.

[0038] 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 from the device. Then, an intelligent scheduling algorithm based on ant colony optimization algorithm is proposed to build a dynamic scheduling model, and the traffic scheduling problem is modeled as an optimization problem, with the goal of optimizing the total time and communication overhead of traffic transmission while meeting the deadline.

[0039] S3, build a dynamic scheduling model to obtain adjusted traffic priority, allocated time slots and bandwidth resources, including: S3, build a dynamic scheduling model to obtain adjusted traffic priority, allocated time slots and bandwidth resources, including: 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, 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 influencing factor of the equipment status; 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; S331, according to the pheromone concentration obtained in step S322 , the inspiration 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, is the newly added pheromone concentration; 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 are adapted to the current network conditions.

[0040] Preferably, the step S34 includes: S341, according to the formula: , Calculate the fitness of each candidate scheduling solution ;in, , , , 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.

[0041] Preferably, the step S1 also includes 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.

[0042] The present invention also provides 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, wherein 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.

[0043] 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.

[0044] In view of the deficiencies and improvement needs in the prior art, the TSN-based industrial equipment collaborative control method provided by the present invention aims to solve the problems of poor real-time performance and inflexible traffic scheduling in the equipment collaborative control of the existing industrial network. In general, the technical solution conceived by the present invention can achieve the following beneficial effects compared with the prior art: The present invention adopts a static traffic scheduling strategy, which ensures that key control traffic can be transmitted first by setting different priorities and time slot allocation rules for different types of traffic, thereby improving the real-time control capability of industrial equipment. The present invention adopts an intelligent traffic dynamic 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.

[0045] Figure 4 FIG. 1 is a schematic diagram of a hardware structure for running a TSN-based industrial equipment collaborative control method provided by an embodiment of the present invention. Figure 4 As shown, the 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 in the above-mentioned method embodiments are implemented. Alternatively, when the processor 60 executes the computer program 62, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0046] Exemplarily, 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.

[0047] The computer 6 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer 6 may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will appreciate that Figure 4 It is only an example of computer 6 and does not constitute a limitation of 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.

[0048] 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), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0049] The memory 61 may be an internal storage unit of the computer 6, such as a hard disk or memory of the computer 6. The memory 61 may also be an external storage device of the computer 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD), a Flash Card (FC), etc. equipped on the computer 6. Further, the memory 61 may include both an 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 may also be used to temporarily store data that has been output or is to be output.

[0050] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be 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 in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in 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, which will not be repeated here.

[0051] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0052] 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. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0053] 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 only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, 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.

[0054] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0055] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0056] If the integrated module / unit is implemented in the form of 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 implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, 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 contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0057] 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 the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A TSN-based industrial equipment collaborative control method, characterized in that: Includes steps: S1, the switch obtains the TSN network from the preset start detection time To the current time of traffic, and The traffic obtained at any time is evenly divided into N traffic , as of the current time , 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 ith flow, represents the jitter of the ith 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; S22, obtaining the traffic priority, allocated time slot and bandwidth resources that minimize the optimization objective function in step S21; S3, build a dynamic scheduling model to obtain adjusted traffic priority, allocated time slots and bandwidth resources, including: 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, 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 influencing factor of the equipment status; 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; S331, according to the pheromone concentration obtained in step S322 , the inspiration 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, is the newly added pheromone concentration; 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 are adapted to the current network conditions.

2. The TSN-based industrial equipment collaborative control method according to claim 1, characterized in that: In the step S2, ， ， ； 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 ith flow respectively.

3. The TSN-based industrial equipment collaborative control method according to claim 2, characterized in that: According to the i-th flow The latency, jitter, and packet loss rate of the data are counted up to the current time. 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.

4. The TSN-based industrial equipment collaborative control method according to claim 1, characterized in that: The step S2 further includes the step of: according to the current time 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.

5. The TSN-based industrial equipment collaborative control method according to claim 4 is characterized in that: The step S2 also 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: ， ； in, represents the bandwidth requirement of the ith flow, is the total bandwidth of the TSN network, represents the transmission time slot requirement of the ith flow, is the total number of time slots in the TSN network.

6. The TSN-based industrial equipment collaborative control method according to claim 1, characterized in that: The step S34 comprises: 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.

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

8. 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 as described in any one of claims 1 to 7 are implemented.

9. 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 as described in any one of claims 1 to 7 are implemented.

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