Stability control method and equipment of adaptive cruise control system based on time-sensitive network, medium and product

Abstract

The invention discloses a stability control method and device of an adaptive cruise control system based on a time-sensitive network, a medium and a product, and relates to the field of adaptive cruise control, and the method comprises the steps: determining an optimization target; determining a gating list; obtaining a solution vector and decoding the solution vector into a windowing length and a windowing offset; carrying out fine tuning on the decoded solution vector; solving the optimization target based on the decoded and fine-tuned solution vector to obtain the worst end-to-end delay of the stream; solving the optimization target based on the worst end-to-end delay of the flow and a genetic algorithm to obtain a scheduling scheme with the lowest delay; constructing a closed-loop system matrix considering time delay based on the scheduling scheme with the lowest time delay; the characteristic value of the closed-loop system matrix considering the time delay is located in a unit circle; the adaptive cruise control system is controlled based on the closed-loop system matrix considering the time delay, the stability of the TSN to the adaptive cruise control system can be effectively evaluated, and a scheduling scheme for ensuring the stability of the adaptive cruise control system is output.

Description

Technical Field

[0001] The present application relates to the field of adaptive cruise control, and in particular to a stability control method, device, medium and product of an adaptive cruise control system based on a time-sensitive network. Background Art

[0002] In the wave of vehicle intelligence, vehicles are equipped with more and more intelligent sensors, controllers and actuators. This trend has promoted the evolution of electronic and electrical architecture from distributed to centralized, giving rise to central computing platforms and regional controllers with higher computing power, as well as vehicle backbone networks with larger bandwidth. For the sake of safety and control stability, the in-vehicle communication system must be deterministic and real-time. Time-sensitive networking (TSN), which is extended to Ethernet, achieves deterministic low-latency communication through mechanisms such as clock synchronization and time-aware scheduling (TAS), and is considered to be the most promising solution for the next generation of vehicle networks. A large number of studies have shown that TSN can significantly reduce the latency and jitter of time-triggered streams, and has obvious communication advantages over traditional CAN and Ethernet. However, the specific impact of this communication advantage on common vehicle controls (such as adaptive cruise control, ACC) is still lacking in sufficient discussion. Therefore, in the context of TSN, how to design ACC control stability has become a problem worthy of attention. Summary of the invention

[0003] The purpose of this application is to provide a stability control method, device, medium and product for an adaptive cruise control system based on a time-sensitive network, which can realize communication optimization design through information flow scheduling to achieve ACC stability control under complex communication loads.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a stability control method for an adaptive cruise control system based on a time-sensitive network, and the stability control method for an adaptive cruise control system based on a time-sensitive network includes:

[0006] Determine optimization goals;

[0007] Determine a gating list; the gating list includes: a gating list period constraint, a window length constraint for each priority level, and a window bias constraint;

[0008] Obtain a solution vector; the solution vector includes a set of real numbers;

[0009] Decoding each real number in the solution vector into a window length and a window bias;

[0010] Fine-tune the decoded solution vector so that the window length and window bias of each real number in the decoded solution vector meet the window length constraint and window bias constraint of each priority level;

[0011] Solving the optimization objective based on the decoded and fine-tuned solution vector to obtain the worst end-to-end delay of the flow;

[0012] Solving the optimization objective based on the worst end-to-end delay of the flow and a genetic algorithm to obtain a scheduling solution with the lowest delay;

[0013] Constructing a closed-loop system matrix taking into account delay based on the scheduling scheme with the lowest delay; wherein the eigenvalue of the closed-loop system matrix taking into account delay is within the unit circle;

[0014] The adaptive cruise control system is controlled based on the closed-loop system matrix considering the delay.

[0015] Optionally, the optimization objective is expressed as follows:

[0016]

[0017] Among them, J is the optimization target, f i is flow i, F is the set of flows, D fi is the worst end-to-end delay of the flow.

[0018] Optionally, the expressions of the gating list period constraint, the window length constraint of each priority level, and the window bias constraint are as follows:

[0019] T GCL ≤min({f i .D|f i ∈F})

[0020]

[0021] HPmodT GCL =0

[0022] Among them, T GCL represents the gate list period, f i .D is the solution deadline of the flow, f i is flow i, F is the set of flows, P m is the priority m, P is the set of all priorities, Yes oPort P m The window length of the priority, O is the set of all ports, HP is the super period;

[0023]

[0024] in, is the window length, f i .size is the size of the stream, f i .T is the period of the flow, Indicates that the transmission priority through port o is P m is the set of flows, B is the network bandwidth, is the length of the protection belt;

[0025]

[0026] Among them, P o is the sum of the priorities on o, is the window length of the BE flow at port o, is the set of BE flows transmitted through port o;

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] in, is the window opening time offset of the priority m window on port o, P n is the priority n, is the window opening time offset of the priority n window on port o, It is the window length of the priority n window on port o.

[0033] Optionally, the length of the solution vector is:

[0034]

[0035] Among them, O represents the set of all ports, o represents the current port, and P o Represents the sum of the priorities on o.

[0036] Optionally, decoding each real number in the solution vector into a window length and a window offset is specifically performed using the following formula:

[0037]

[0038]

[0039] in, and They are the corresponding o ports P in the solution vectorm Real numbers for the length and offset of the window.

[0040] Optionally, the optimization objective is solved based on the decoded and fine-tuned solution vector to obtain the worst end-to-end delay of the stream using the following formula:

[0041]

[0042] Among them, D fi represents the worst end-to-end delay, O represents the set of all ports, o represents the current port, represents the queuing and transmission delay of the flow with priority m at port o, t prop represents the propagation delay, t proc Indicates processing delay.

[0043] The control delay of the previous condition ACC is expressed as D ACC =(Υ+ν)·t s , where t s To control the cycle,

[0044] Optionally, the closed-loop system matrix considering the delay is expressed as follows:

[0045]

[0046] in, Both represent the discrete coefficient matrix of the ACC state space equation, Δ i,k represents the uncertainty coefficient matrix introduced by random time-varying delay, which can be processed by matrix polytope description method, i=1, ..., γ, K represents the optimal control matrix calculated by the linear quadratic regulator (LQR) based on the state space equation without considering the delay.

[0047] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the stability control method of an adaptive cruise control system based on a time-sensitive network as described above.

[0048] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for stability control of an adaptive cruise control system based on a time-sensitive network.

[0049] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for stability control of an adaptive cruise control system based on a time-sensitive network.

[0050] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0051] The present application provides a stability control method, device, medium and product for an adaptive cruise control system based on a time-sensitive network. As the load of traditional Ethernet increases, the delay of key control links increases significantly. Under the same BE flow load, the delay of TSN is much smaller than that of Ethernet. The scheduling method in the present application reduces the interference between flows through reasonable window division, and provides real-time and deterministic transmission for TT flows. As the delay of Ethernet increases under high load, points with eigenvalues ​​that are not within the unit circle will appear. Therefore, simple LQR control cannot achieve system stability. This situation requires a more complex robust control design. The TSN delay scheduled in the present application is The characteristic roots are all distributed within the unit circle, and stable control can be achieved through LQR even under high BE flow load. In addition, many eigenvalues ​​of Ethernet delay have a large imaginary part, which will cause the system response to show more significant oscillation behavior, while the eigenvalues ​​of TSN delay are distributed around the real axis and will not show significant oscillation behavior, which is conducive to the smooth response of the system; the stability of control no longer needs to be guaranteed by complex control methods. The low latency of the TSN mechanism enables the system to have better control stability in complex load environments. The introduction of TSN can further simplify the design of the control system, that is, a simple control strategy can be used to achieve the functions of the control strategy that originally required a robust design. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 A schematic diagram of a stability control method of an adaptive cruise control system based on a time-sensitive network in an embodiment of the present application;

[0054] Figure 2 A schematic flow chart of a stability control method of an adaptive cruise control system based on a time-sensitive network in an embodiment of the present application;

[0055] Figure 3This is a schematic diagram of the encoding and decoding process in an embodiment of the present application;

[0056] Figure 4 This is a schematic diagram of information transmission of a network and a control process in an embodiment of the present application;

[0057] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. 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 embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0060] The present application provides a stability control method for an adaptive cruise control system based on a time-sensitive network. The framework of the method is as follows: Figure 1 As shown in the figure, it mainly consists of two parts. The first part is a scheduling method based on the worst-case delay analysis theory of flexible windows, and the second part is an ACC system stability analysis theory based on the worst-case control link delay of TSN. The first part of the scheduling method outputs the delay to the second part of the stability analysis for system stability determination. If the determination fails, it is scheduled again until the system is stable.

[0061] See also Figure 2 The detailed steps of the stability control method of the adaptive cruise control system based on a time-sensitive network in this application are as follows:

[0062] Part 1: Network Scheduling Based on Flexible Window Worst-Case Delay Analysis Theory

[0063] The basic process of scheduling is to map the scheduling solution into a gated list scheme, which uses the worst-case delay analysis theory to evaluate the scheme and further iteratively search the scheduling solution based on the evaluation results. The genetic algorithm solution process is very consistent with the scheduling method of this application. The delay analysis process can be regarded as a fitness function solution process, and the iterative search process can be implemented through crossover and compilation.

[0064] Step 101: Determine the optimization target.

[0065] In order to simplify the design process, the present application makes the following provisions: First, all ports use the same GCL cycle, and different priority windows of the same port are not allowed to overlap with each other. Although some existing methods provide analysis methods for different cycles and window overlaps, the present application believes that these methods are unnecessary during design, because this not only increases the difficulty of analysis, but also the occupation of high priority may have an adverse effect on the delay of low priority. Secondly, after reserving enough bandwidth for the best effort flow (BE flow), the time-sensitive flow occupies all the remaining bandwidth. This is because the in-vehicle network scenario is relatively stable and there will not be a large amount of unpredictable traffic. Therefore, occupying all bandwidth can effectively reduce bandwidth waste and reduce scheduling delay. Finally, the present invention does not only focus on ACC-related information flows during scheduling, but also focuses on the transmission effect of the overall TT flow. Therefore, the scheduling optimization goal is to minimize the delay of all TT flows, as shown in formula (1).

[0066]

[0067] Among them, J is the optimization target, D fi is the worst end-to-end delay of the flow.

[0068] Step 102: Determine a gating list; the gating list includes: a gating list period constraint, a window length constraint for each priority level, and a window offset constraint.

[0069] After the network environment is determined, the gating list of an output port can be determined by the gating list period T GCL , the window length for each priority and window bias The proper selection of the gating list period is crucial for successful scheduling. In order to meet the deadline constraints of the flow, T GCL must not exceed the minimum value of the deadline of all flows, as shown in formula (2). At the same time, T GCL It needs to be long enough to accommodate all priority windows, so it must be no less than the sum of the lengths of all priority windows of each output port, as shown in equation (3), where O represents the set of all output ports. In addition, for ease of deployment, T GCL It needs to be a divisor of the super period, as shown in formula (4).

[0070] T GCL ≤min({f i .D|f i ∈F})(2)

[0071]

[0072] HPmodT GCL =0(4)

[0073] Among them, f i .D is the solution deadline of the flow, F is the set of flows, P m is the priority m, P is the set of all priorities, Yes oPort P m The priority window length, O is the set of all ports, and HP is the super period.

[0074] definition and The P of port o is m The window length and window offset of the priority window. In order to ensure that the window has enough bandwidth to complete the transmission, Need to satisfy the constraints defined in formula (5)

[0075]

[0076] Among them, f i .size is the size of the stream, f i .T is the period of the flow, Indicates that the transmission priority through port o is P m is the set of flows, B is the network bandwidth, is the length of the guard band, as shown in formula (6).

[0077]

[0078] Wherein, MTU is the maximum transmission unit, and its size is 1500B. Since the BE flow does not need to guarantee its transmission delay, this application only reserves sufficient transmission bandwidth for the BE flow, and its window length is shown in formula (7).

[0079]

[0080] As defined above, time-sensitive flows occupy all bandwidth except BE flows, so It is also necessary to satisfy the following constraint (8).

[0081]

[0082] Window offset is the window relative to T GCL The window time of the start time. In order to ensure that all window periods are within the cycle, The constraints shown in formula (9) need to be satisfied.

[0083]

[0084]

[0085]

[0086] Since the present invention also defines that windows of different priorities are not allowed to overlap, and It is also necessary to satisfy the constraints shown in formula (10).

[0087]

[0088] Step 103: Obtain a solution vector; the solution vector includes a set of real numbers.

[0089] Step 104: Decode each real number in the solution vector into a window length and a window offset.

[0090] Step 105: fine-tune the decoded solution vector so that the window length and window bias of each real number in the decoded solution vector meet the window length constraint and window bias constraint of each priority level.

[0091] The scheduling method proposed in this application uses real number coding to represent a solution, that is, a solution vector consists of a set of real numbers, and its length (L) is shown in formula (11), where the first L / 2 bits represent the window length of each priority of each sending port. The last L / 2 digits represent the window bias. The value range of each real number is [1, 1000]. The process of mapping the solution vector to the gating list scheme of each port is called decoding.

[0092]

[0093] Below through Figure 3 Taking the topology shown in FIG. 1 as an example, the encoding and decoding process is described in detail. In this topology, there are three priority streams transmitted from terminal 1 to terminal 2 and terminal 3. There are three output ports in total, which have 3, 1 and 2 priority windows respectively. Therefore, L = 2 × (3 + 1 + 2) = 12. As shown in the solution vector example in the figure, the first 6 bits 1-3 correspond to the window lengths of the three priorities of o1, the fourth bit corresponds to the window length of the 1 priority of o2, and the fifth and sixth bits correspond to the window lengths of the two priorities of o3. The last six bits are similar to the first six bits and correspond to the window opening bias of the window respectively. Using equations (12) and (13), the real number of each bit can be decoded into the window opening length and bias.

[0094]

[0095] in, and They are the corresponding o ports P in the solution vector m Real numbers for the length and offset of the window.

[0096] On this basis, fine-tuning is performed to satisfy the constraints expressed by equations (5), (8), (9), and (10), thereby obtaining all parameters for worst-case delay analysis. Then, the fitness of the solution vector can be obtained using the worst-case delay analysis method based on a flexible window.

[0097] Step 106: Solve the optimization target based on the decoded and fine-tuned solution vector to obtain the worst end-to-end delay of the stream.

[0098] The fitness of this application is the optimization target shown in formula (1). This application solves the delay based on the GCL worst delay analysis theory of flexible window according to the GCL window length and offset obtained by decoding. The priority is P m The flow i The worst delay at port o can be calculated by formula (14),

[0099]

[0100] in, and Respectively represent P m The arrival curve and service curve of the window, h(·) represents the maximum horizontal distance between the two curves. Based on formula (15), we can further obtain f i The end-to-end transmission delay is shown in (15).

[0101]

[0102] Among them, O(f i ) is f i The set of sending ports that the route passes through, |O(f i )| is the number of sending ports in the set, t prop , t proc They represent propagation delay and processing delay respectively. Since the in-vehicle network transmission distance is short and the switch chip performance is high, this application ignores these two delays. and The specific calculation method of can refer to the existing TSN analysis method based on network calculation, and is not specifically limited or defined in this application.

[0103] Step 107: Solve the optimization target based on the worst end-to-end delay of the flow and the genetic algorithm to obtain a scheduling solution with the lowest delay.

[0104] In this application, a genetic algorithm is used for solving the problem. The initialization population is generated through real number coding. The optimal solution is continuously searched after crossover mutation. The search is stopped after the specified solution time is reached, and the optimal solution is output as the scheduling result.

[0105] Part II: Stability analysis of ACC system based on the worst control link delay of TSN

[0106] Step 108: constructing a closed-loop system matrix taking into account delay based on the scheduling scheme with the lowest delay; the eigenvalue of the closed-loop system matrix taking into account delay is located within the unit circle.

[0107] The scheduling method proposed above can calculate the worst delay of the ACC control chain flow, which can be further expressed as D ACC =(Υ+ν)·t s . Where t s is the controller period. The ACC closed-loop state space equation without considering the control delay is shown in equation (16).

[0108]

[0109] Since the ZCU executes the latest control command received in the event-triggered mode, the control commands that may act on the system in the time period from k to k+1 include u k ,u k-1 ,...u k-Υ-1 The system state equation (16) considering the delay can be rewritten as (17)

[0110]

[0111] Among them, Δ i,k is the uncertainty coefficient matrix introduced by random time-varying delay. According to state feedback control, Therefore, equation (17) can be further written as the closed-loop system equation (18):

[0112]

[0113] In this application, K is obtained by solving equation (16) based on LQR control.

[0114] Define a new state vector Rewrite equation (18) as equation (19)

[0115] X k+1 =ΦX k +θr k (19)

[0116] in, The value in the lower right corner indicates the dimension of the matrix. Thus, the closed-loop system matrix Φ is constructed.

[0117] Step 109: Control the adaptive cruise control system based on the closed-loop system matrix taking into account the delay.

[0118] According to the stability criterion of discrete control systems, as long as all eigenvalues ​​of Φ are within the unit circle, the asymptotic stability of the system can be guaranteed. Thus, the stability of the ACC control system considering the delay is proved.

[0119] The application scope of the above scheme in this application is as follows:

[0120] (1) Vehicle network: This solution is applicable to centralized electronic and electrical architecture, which needs to include sensors, regional controllers, and high-performance computing units. At the same time, the whole vehicle communication needs to support time-sensitive network related protocols, including IEEE 802.1AS protocol and IEEE 802.Qbv protocol.

[0121] (2) Control process: After the sensor collects data in real time, it transmits the signal to the high-performance computing unit through Ethernet through the regional controller. After the high-performance computing unit obtains the latest signal, it processes it and sends the control signal to the relevant regional controller for execution. The information transmission diagram of the applicable network and control process is as follows: Figure 4 shown.

[0122] (3) ACC control method: The hierarchical control ACC method, in which the upper controller is the acceleration planning layer and the lower controller is the acceleration tracking layer. The upper layer usually decides the expected acceleration based on the safe vehicle distance, and the lower controller uses the LQR algorithm to ensure that the actual acceleration of the vehicle stably follows the expected acceleration command under the influence of delay.

[0123] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data of a stability control method of an adaptive cruise control system based on a time-sensitive network. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a stability control method of an adaptive cruise control system based on a time-sensitive network is implemented.

[0124] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0125] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0126] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0127] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0128] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0129] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0130] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0131] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A stability control method for an adaptive cruise control system based on a time-sensitive network, characterized in that: The stability control method of the adaptive cruise control system based on a time-sensitive network includes: Determine optimization goals; Determine a gating list; the gating list includes: a gating list period constraint, a window length constraint for each priority level, and a window bias constraint; Obtain a solution vector; the solution vector includes a set of real numbers; Decoding each real number in the solution vector into a window length and a window bias; Fine-tune the decoded solution vector so that the window length and window bias of each real number in the decoded solution vector meet the window length constraint and window bias constraint of each priority level; Solving the optimization objective based on the decoded and fine-tuned solution vector to obtain the worst end-to-end delay of the flow; Solving the optimization objective based on the worst end-to-end delay of the flow and a genetic algorithm to obtain a scheduling solution with the lowest delay; Constructing a closed-loop system matrix taking into account delay based on the scheduling scheme with the lowest delay; wherein the eigenvalue of the closed-loop system matrix taking into account delay is located within the unit circle; The adaptive cruise control system is controlled based on the closed-loop system matrix considering the delay.

2. The stability control method of the adaptive cruise control system based on a time-sensitive network according to claim 1, characterized in that: The expression of the optimization objective is as follows: Among them, J is the optimization target, f i is flow i, F is the set of flows, D fi is the worst end-to-end delay of the flow.

3. The stability control method of the adaptive cruise control system based on a time-sensitive network according to claim 1, characterized in that: The expressions of the gating list period constraint, the window length constraint of each priority level and the window bias constraint are as follows: T GCL ≤min({f i .D|f i ∈F}) HPmodT GCL =0 Among them, T GCL represents the gate list period, f i .D is the solution deadline of the flow, f i is flow i, F is the set of flows, P m is the priority m, P is the set of all priorities, Yes oPort P m The window length of the priority, O is the set of all ports, HP is the super period; in, is the window length, f i .size is the size of the stream, f i .T is the period of the flow, Indicates that the transmission priority through port o is P m is the set of flows, B is the network bandwidth, is the length of the protection belt; Among them, P o is the sum of the priorities on o, is the window length of the BE flow at port o, is the set of BE flows transmitted through port o; in, is the window opening time offset of the priority m window on port o, P n is the priority n, is the window opening time offset of the priority n window on port o, It is the window length of the priority n window on port o.

4. The stability control method of the adaptive cruise control system based on a time-sensitive network according to claim 1, characterized in that: The length of the solution vector is: Among them, O represents the set of all ports, o represents the current port, and P o Represents the sum of the priorities on o.

5. The stability control method of the adaptive cruise control system based on a time-sensitive network according to claim 2, characterized in that: The following formula is used to decode each real number in the solution vector into the window length and the window offset: in, and They are the corresponding o ports P in the solution vector m Real numbers for the length and offset of the window.

6. The stability control method of the adaptive cruise control system based on a time-sensitive network according to claim 1, characterized in that: The optimization objective is solved based on the decoded and fine-tuned solution vector to obtain the worst end-to-end delay of the flow using the following formula: in, represents the worst end-to-end delay, O represents the set of all ports, o represents the current port, represents the queuing and transmission delay of the flow with priority m at port o, t prop represents the propagation delay, t proc Indicates processing delay.

7. The stability control method of the adaptive cruise control system based on a time-sensitive network according to claim 1, characterized in that: The expression of the closed-loop system matrix considering the delay is as follows: in, Both represent the discrete coefficient matrix of the ACC state space equation, Δ i,k represents the uncertainty coefficient matrix introduced by random time-varying delay, i=1,...γ, K represents the optimal control matrix calculated by the linear quadratic regulator based on the state space equation without considering the delay.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the stability control method of the time-sensitive network-based adaptive cruise control system according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the stability control method of an adaptive cruise control system based on a time-sensitive network according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the stability control method of an adaptive cruise control system based on a time-sensitive network according to any one of claims 1 to 7 are implemented.

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