Vehicle-mounted tsn communication scheduling method and system for distributed electric drive vehicle

Abstract

The application provides a vehicle-mounted TSN communication scheduling method and system for a distributed electric drive vehicle, and relates to the technical field of vehicle-mounted communication.The application applies TSN technology to the distributed electric drive vehicle to construct a corresponding vehicle-mounted TSN, and constructs a constraint model containing various constraint conditions in combination with the real-time situation of the vehicle-mounted TSN, and then solves the scheduling solution through an SMT solver to provide a deterministic scheduling strategy for the vehicle-mounted TSN.The application adopts two solving modes according to the actual situation, one is to solve all data when the data volume is small, and the other is to divide the data by time windows when the data volume is large, and a backtracking strategy is introduced to optimize the calculation speed and ensure the calculation accuracy.The application not only realizes low delay of high-priority information flow, but also ensures that the delay of other low-priority information flow meets the preset requirements, and can meet the scheduling requirements of the vehicle-mounted network of the distributed electric drive vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle communication, more particularly, it relates to: 1. A vehicle TSN (Time Sensitive Networking) communication scheduling method for a distributed electric drive vehicle; 2. A vehicle TSN communication scheduling system for a distributed electric drive vehicle. BACKGROUND

[0002] With the rapid development of distributed electric drive vehicles, higher requirements are put forward for vehicle networks, mainly in terms of real-time performance, bandwidth, reliability and data processing capability:

[0003] First, the distributed electric drive vehicle requires high real-time performance of the vehicle network. Because the distributed electric drive system involves multiple motors and sensors working together, the vehicle network needs to quickly and accurately transmit control instructions and sensor data. Any delay may cause inaccurate control and affect the stability and safety of the vehicle.

[0004] Second, the bandwidth demand increases significantly. Distributed electric drive vehicles need to transmit much more data than traditional vehicles, involving motor status, battery status, vehicle dynamics parameters, etc. This requires the vehicle network to have higher data transmission rate and larger bandwidth to meet the real-time transmission needs of a large amount of data.

[0005] Third, reliability is a key factor for vehicle networks in distributed electric drive vehicles. Since the distributed electric drive system is very critical to vehicle travel, any network failure may cause system failure and safety problems. The vehicle network must have high reliability and stability to ensure normal operation of the vehicle under various working conditions.

[0006] Existing vehicle network scheduling generally uses traditional scheduling algorithms, such as scheduling based on tabu search algorithm (referred to as TS), scheduling based on genetic algorithm (referred to as GA), scheduling based on adaptive crossover probability algorithm (referred to as PC), etc. However, these algorithms cannot meet the scheduling requirements of distributed electric drive vehicle networks. SUMMARY

[0007] Therefore, it is necessary to provide a vehicle TSN communication scheduling method and system for a distributed electric drive vehicle to solve the problem that existing traditional scheduling algorithms cannot meet the scheduling requirements of distributed electric drive vehicle networks.

[0008] The present application adopts the following technical solutions:

[0009] In a first aspect, the present application discloses a vehicle TSN communication scheduling method for a distributed electric drive vehicle, comprising the following steps:

[0010] Step one, based on the structure of the target vehicle, a vehicle-mounted TSN is constructed, and all data links are extracted;

[0011] Step two, all data streams that are currently appearing in the vehicle-mounted TSN are obtained N and a real-time data stream set is formed;

[0012] Step three, information analysis is performed on each data stream in the real-time data stream set, and the corresponding time delay offset is initialized, and a constraint condition is added to the real-time data stream set to obtain a constraint model;

[0013] Step four, if N < N 0, the SMT solver is used to directly solve the constraint model to obtain a scheduling solution; N 0 represents a quantity threshold;

[0014] If N ≥ N 0, the data frame selection is performed on the constraint model through a sliding window; the SMT solver is used to solve the data selected by the current window to obtain a current solution, and then the SMT solver is used to solve the data selected by the next window in combination with the current solution; if a feasible solution is found, the feasible solution is updated as the current solution; if a feasible solution cannot be found, the current window is backtracked, the range of the current window is expanded, and the solving is performed again; the data frame selection and the solving are continuously performed until N all data streams are processed, and the current solution at this time is taken as the scheduling solution.

[0015] The vehicle-mounted TSN communication scheduling method for the distributed electric drive vehicle realizes the method or process according to the embodiments of the present disclosure.

[0016] In a second aspect, the present disclosure discloses a vehicle-mounted TSN communication scheduling system for a distributed electric drive vehicle, which uses the vehicle-mounted TSN communication scheduling method for a distributed electric drive vehicle disclosed in the first aspect.

[0017] The vehicle-mounted TSN communication scheduling system for a distributed electric drive vehicle comprises a preprocessing module, a data acquisition module, a problem construction module, and a scheduling solving module.

[0018] The preprocessing module is configured to construct a vehicle-mounted TSN based on the structure of a target vehicle, and extract all data links.

[0019] The data acquisition module is configured to obtain all data streams that are currently appearing in the vehicle-mounted TSN N and form a real-time data stream set.

[0020] The model building module is used to analyze the information of each data stream in the real-time data stream set, initialize its corresponding time delay offset, and then add constraints to the real-time data stream set to obtain a constrained model.

[0021] The scheduling solution module is used for: if N < N If 0, then the SMT solver is used to directly solve the constraint model to obtain the scheduling solution; if N ≥ N If the value is 0, the data frame for the constraint model is selected using a sliding window; the SMT solver is used to solve the data selected in the current window to obtain the current solution. Then, the SMT solver is used in conjunction with the current solution to solve the data selected in the next window; if a feasible solution is found, it is updated as the current solution; if no feasible solution is found, the process backtracks to the current window, expands the scope of the current window, and solves again; this process of data selection and solving continues until... N All data streams have been processed, and the current solution is used as the scheduling solution.

[0022] This onboard TSN communication scheduling system for distributed electric vehicles implements the methods or processes according to embodiments of this disclosure.

[0023] Thirdly, the present invention discloses a computer program product, including a computer program. When executed by a processor, the computer program implements the steps of the on-board TSN communication scheduling method for distributed electric drive vehicles disclosed in the first aspect.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. This invention applies TSN technology to distributed electric drive vehicles to construct a corresponding on-board TSN. It also constructs a constraint model with various constraints based on the real-time status of the on-board TSN, and then solves the model using an SMT solver to obtain a scheduling solution, providing a deterministic scheduling strategy for the on-board TSN. Simulation results show that this invention not only achieves low latency for high-priority information flows but also ensures that the latency of other low-priority information flows meets preset requirements, thus satisfying the scheduling requirements of the on-board network for distributed electric drive vehicles.

[0026] 2. This invention adopts two solution methods based on actual conditions. One is to use all data to solve when the amount of data is small, and the other is to use time window to divide the data to solve when the amount of data is large. A backtracking strategy is also introduced to optimize the calculation speed and ensure the calculation accuracy. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of the on-board TSN communication scheduling method for distributed electric drive vehicles proposed in Embodiment 1 of the present invention;

[0029] Figure 2 Example diagram for the target vehicle in Example 1 when only one central switch is installed;

[0030] Figure 3 A schematic diagram of setting up two central switches for the target vehicle provided in Example 1;

[0031] Figure 4 for Figure 3 The corresponding in-vehicle TSN diagram. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] It should be noted that when a component is said to be "installed on" another component, it can be directly on the other component or it may be in a component that is centered on it. When a component is said to be "set on" another component, it can be directly set on the other component or it may also be in a component that is centered on it. When a component is said to be "fixed to" another component, it can be directly fixed to the other component or it may also be in a component that is centered on it.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0035] First, it should be noted that the present invention aims to provide a vehicle network communication scheduling method for distributed electric drive vehicles to meet the scheduling requirements of the vehicle network of distributed electric drive vehicles.

[0036] Example 1

[0037] See Figure 1 This is a flowchart of the on-board TSN communication scheduling method for distributed electric drive vehicles provided in Embodiment 1.

[0038] like Figure 1 As shown, this communication scheduling method includes the following steps:

[0039] Step 1: Construct the vehicle-mounted TSN based on the structural composition of the target vehicle and extract all data links.

[0040] As mentioned above, this communication scheduling method targets distributed electric drive vehicles—physically, the target vehicles include: M Individual terminal devices (e.g., lidar, cameras, motors, controllers, etc.) N A central switch (which can be regarded as a relay station for data transmission).

[0041] See Figure 2 It showed M =8、 N One specific scenario with =1: The 8 terminal devices are Lidar, Actuators (covering power domain controller and motor), VIU1 (environment domain controller), VIU2 (network domain controller), Camera, CDC (intelligent cockpit domain controller), MDC (intelligent driving domain controller), and VCU (vehicle controller); and 1 central switch is Switch (network switch); these 8 terminal devices are connected to Switch via physical cables. During data transmission, data from one terminal device is first transmitted to Switch, and then Switch transmits it to another terminal device.

[0042] See Figure 3 It showed M =6、 N One scenario with a value of 2 includes: 6 terminal devices A1~A6 and 2 central switches B1~B2; A1~A3 are physically connected to B1; B1 and B2 are physically connected; A4~A6 are physically connected to B2. Therefore, data transmission between A1 and A2 must pass through B1; data transmission between A1 and A4 must pass through both B1 and B2; other scenarios are similar and will not be elaborated further.

[0043] It should be noted that if N >2, In order to improve communication efficiency, NEach central switch uses a switched connection rather than a bus connection, meaning that any two central switches can communicate with each other via physical cables; a single terminal device can only communicate with one central switch via a physical cable.

[0044] TSN is a protocol specification developed based on traditional Ethernet. By defining different traffic categories, it ensures that the communication needs of various automotive application scenarios are met. When dealing with distributed electric drive vehicles, TSN can be applied to the target vehicle as the basis for building communication scheduling methods.

[0045] Specifically, the on-board TSN corresponding to the target vehicle includes: M Each terminal node N Each network element node corresponds to a single terminal device and a single central switch.

[0046] Based on the above physical structure description, we have: the first m Each terminal node is connected to its corresponding network element node and supports bidirectional communication; m ∈[1, M ]. And it should be noted that, if N If the value is greater than 1, then any two network element nodes can be interconnected and support bidirectional communication.

[0047] Since a vehicle-mounted TSN is composed of nodes, two adjacent nodes in a vehicle-mounted TSN, with no other nodes between them, form a single data link. This provides the transmission foundation for end-to-end transmission in the vehicle-mounted TSN. In other words, two adjacent nodes in a vehicle-mounted TSN, with no other nodes between them, serve as the two endpoints of a single data link.

[0048] Based on the above design, data transmission between terminal devices in the target vehicle corresponds to end-to-end transmission in the in-vehicle TSN—that is, the generation of data streams between terminal nodes, and the path of the data stream includes several data links. In other words, a data stream follows its corresponding data link for transmission; of course, a single data stream can be regarded as including: several data frames; the two endpoints of a single data frame are the same as the two endpoints of its corresponding data link.

[0049] In general, a single data link is bidirectional; a single data stream is unidirectional; and a single data frame is also unidirectional.

[0050] For ease of understanding, Figure 3 Taking the physical structure as an example, construct such as Figure 4The vehicle-mounted TSN includes: 6 terminal nodes a1~a6 and 2 network element nodes b1~b2. a1~a6 correspond one-to-one with A1~A6; b1~b2 correspond one-to-one with B1~B2. Now, assuming... Figure 2 If data transmission occurs from A3 to A4, then... Figure 3 The corresponding data stream will be a1→b1→b2→a4, which includes 3 data frames: a1→b1, b1→b2, b2→a4. It can also be regarded as unidirectional transmission on 3 data links (including: a1↔b1, b1↔b2, b2↔b4).

[0051] Step 2: Obtain the real-time information displayed in the vehicle's TSN. N These data streams are combined to form a real-time data stream set.

[0052] The generation of data streams is determined based on the actual situation of the vehicle-mounted TSN. As described above, if an end-to-end transmission occurs between two terminal devices, a corresponding data stream will be generated in the vehicle-mounted TSN. When multiple data streams occur, and they all pass through the same network element node, interference will occur between the data streams if they are not scheduled, which can easily lead to packet loss and excessively long delays for some data.

[0053] This step involves acquiring and scheduling these data streams. It's crucial to ensure the data streams contain correct data types and values ​​to prevent transmission errors caused by problems with the data itself.

[0054] Step 3: Perform information analysis on each data stream in the real-time data stream set, initialize its corresponding time delay offset, and then add constraints to the real-time data stream set to obtain a constraint model.

[0055] Information analysis mainly consists of two aspects:

[0056] ① Priority division:

[0057] As described above, a single data stream is actually an end-to-end transmission between two terminal devices. Different data streams have different latency requirements, so the data streams are prioritized before scheduling.

[0058] Specifically, the division can be based on the following principles:

[0059] The smaller the maximum latency tolerance value for a data stream, the higher its priority; conversely, the larger the maximum latency tolerance value, the lower its priority. For example, the maximum latency tolerance value for an engine signal is extremely low, less than 100 microseconds, so its priority is the highest level; the maximum latency tolerance value for a cloud service signal is very high, depending on the actual wireless communication situation, so its priority is the lowest level. It should be noted that the number of priority levels can be adjusted according to the actual situation, generally set to 3 to 8.

[0060] Of course, the dependencies between data streams can also be considered to determine priorities—for two data streams... Data 1~ Data 2, if Data 1 needs to Data 2. Transmission can only proceed after the transmission is complete, so let's set it up. Data 2 has higher priority than Data Priority of 1.

[0061] ② Data Analysis:

[0062] Data streams also contain information such as data size and data cycle. By analyzing each data stream, this information can be obtained as preparation work before scheduling and solving.

[0063] After completing the information analysis, the corresponding time delay offset can be initialized for the data stream—the specific operation is as follows:

[0064] Each data stream in the real-time data stream set is decomposed into several data frames, and a delay offset is initialized for each data frame;

[0065] Specifically, for a given data frame, the delay offset of its starting point (i.e., the time when transmission begins) is initialized, and then the delay offset of its ending point (i.e., the time when transmission is completed) is calculated based on the data information transmitted in the data frame and the transmission speed.

[0066] Therefore, the real-time data stream set can actually be viewed as a set of real-time data frames, where each data frame is initialized with a delay offset.

[0067] To enable subsequent solutions, constraints need to be added to the real-time data stream set. These constraints are limitations that ensure the normal operation of the vehicular TSN and include: conflict-free constraints, path dependency constraints, switch memory constraints, end-to-end transmission constraints, and application-level constraints.

[0068] I. Collision-free constraints are used to ensure that different data streams transmitted on the same data link do not conflict in time. Collision-free constraints traverse all data links and data streams. For each pair of data streams that may conflict, the collision-free constraint guarantees that their transmission times are staggered.

[0069] Specifically, if there are two data streams f i , f j If data is transmitted over the same data link, then the time delay offsets at their starting points must match: f i After the transmission is complete f j Only then can we begin, or f j After the transmission is complete f i Only then can it begin, thus preventing data packet collisions and resource contention, and ensuring network stability and reliability.

[0070] Conflict-free constraints can be expressed by the formula:

[0071] ;

[0072] In the formula, v k , v l This indicates two different nodes in the vehicle's TSN; Indicates from v k arrive v l Data links; L This represents the set of all data links in the vehicle-mounted TSN; f i , f j This represents two distinct data streams within a set of real-time data streams; F Represents a collection of real-time data streams; a , b All are period multiple counters; LCM ( F . period () represents the least common multiple of the transmission cycles of all data streams; f i . period express f i The transmission period; f j . period express f jThe transmission period; express f i exist Transmission instances on; offset This indicates the transmission start delay offset.

[0073] II. Path dependency constraints are used to ensure that the transmission delay of a single data frame from its origin to its destination does not exceed a preset maximum frame delay. Path dependency constraints consider a single-hop dimension—that is, from the origin to the destination of the data frame, or from one terminal device to the next. Path dependency constraints iterate through all data frames, calculate the transmission delay of each data frame, and guarantee that it does not exceed the preset maximum frame delay.

[0074] Path dependency constraints can be expressed by the formula:

[0075] ;

[0076] In the formula, vl Indicates a data frame; VL This represents the set of all data frames in the real-time data stream. p i express f i The transmission path; , express p i Two adjacent data frames; max ( hopdelay ) indicates the maximum allowable delay for a single hop.

[0077] III. Switch memory constraints are used to ensure that the buffers on network element nodes do not exceed the memory limits of the central switch. Switch memory constraints are crucial for maintaining network stability and preventing memory overflow. Switch memory constraints iterate through all data frames, paying particular attention to data frames involving network element nodes—limiting the buffer time of data frames on the central switch (i.e., the length of time they remain in the central switch's memory) to ensure that it does not exceed the central switch's memory capacity.

[0078] The memory constraint of a switch can be expressed by the formula:

[0079] ;

[0080] In the formula, membound This indicates the maximum memory buffer time for the switch.

[0081] IV. End-to-end transmission constraints are used to ensure that the overall transmission delay of a single data stream from its origin to its destination does not exceed a preset maximum stream delay. End-to-end transmission constraints are crucial for the timeliness requirements of vehicular TSN. End-to-end transmission constraints iterate through all data streams, calculating the overall transmission delay from its origin to its destination to ensure it does not exceed the preset maximum stream delay.

[0082] End-to-end transmission constraints can be expressed by the formula:

[0083] ;

[0084] In the formula, last ( f i )express f i The last data frame in the transmission path; first ( f i )express f i The first data frame in the transmission path; max ( latency ) indicates the maximum permissible end-to-end delay.

[0085] V. Application-level constraints are used to ensure that data streams are transmitted in descending order of priority. Application-level constraints cover the transmission order of specific data streams, meaning that higher-priority data streams must be transmitted before lower-priority data streams.

[0086] Application-level constraints can be expressed by the following formula:

[0087] ;

[0088] In the formula, f a , f b This represents two distinct data streams within a set of real-time data streams, and f b The priority is higher than f a Priority; Δ represents the minimum time interval.

[0089] Therefore, the constraint model includes the five constraints mentioned above, and can be regarded as a scheduling problem to be solved.

[0090] Step four, based on the size of N (i.e., considering...) N and N The relationship of 0, N 0 indicates the quantity threshold), select the corresponding solution mode.

[0091] Specifically:

[0092] Ⅰ. If N < N 0 indicates that the number of flow items is not large, so the SMT solver is used to directly solve the constraint model to obtain the scheduling solution.

[0093] The recommended SMT solver is the z3-solver, which solves the constraint model to obtain the scheduling solution—that is, the delay offset of each data frame (i.e., the delay allocation value of the relevant nodes in the corresponding real-time data stream set in the vehicle TSN). Then, by organizing the scheduling solution, we obtain the transmission time, scheduling period, and gating list of network element nodes for each data stream (i.e., controlling the orderly passage of data streams).

[0094] II. If N ≥ N A value of 0 indicates a large number of data streams. In this case, the scale of the constraints grows exponentially. If the solution is still applied to all data, the SMT solver will have to process a large number of complex constraints at the same time, which will increase the solution time and may even prevent a feasible solution from being found within a reasonable time.

[0095] Therefore, in this case, a sliding window is used to select data frames for the constraint model, thereby dividing the large batch of data streams into small batches of data streams corresponding to the sliding window.

[0096] The SMT solver is then used to solve the data selected in the current window to obtain the current solution. Next, the SMT solver, combined with the current solution, is used to solve the data selected in the next window. If a feasible solution is found, it is updated as the current solution. If no feasible solution is found, the process backtracks to the current window, expands its range, and re-solves. This process of selecting and solving data continues until... N All data streams have been processed, and the current solution is used as the scheduling solution.

[0097] The recommended solver for SMT is the z3-solver. It continuously solves the small batches of data and combines a backtracking mechanism to ensure high solution accuracy while efficiently processing large-scale data streams. Then, by organizing the scheduling solutions, we can obtain the sending time, scheduling cycle, and gating list of network element nodes for each data stream.

[0098] Of course, you can also set a limit on the number of backtracking attempts. If the limit is reached, the solution will stop and a message indicating that the solution cannot be solved will be displayed to avoid the solution getting stuck in an infinite loop.

[0099] Simulation verification

[0100] To illustrate the effectiveness and superiority of the present invention, this embodiment 1 uses... Figure 2 A simulation experiment was conducted using an example:

[0101] 1. There are 18 data streams, divided into high, medium, and low priorities. Among them, all control streams are high priority; all monitoring streams are medium priority; and other data streams are ordinary data streams with low priority.

[0102] First, the scheduling method of Example 1 was used to examine the average latency, latency standard deviation, and packet loss rate of the data stream. Simulation results show that:

[0103] ① The average latency of the data stream is controlled between 0.03ms and 0.25ms, which fully meets the stringent requirements for data streams in distributed electric drive vehicles.

[0104] ② The standard deviation of the delay for high-priority data streams is controlled within 0.01ms to 0.05ms, with relatively small fluctuations; while the standard deviation of the delay for medium-priority data streams is controlled within 0.01ms to 0.02ms and the standard deviation of the delay for low-priority data streams is controlled within 0.01ms to 0.03ms, with more stable fluctuations; indicating that the vehicle-mounted TSN has high performance stability and strong reliability.

[0105] ③ The packet loss rate of high-priority data streams is between 0.001% and 0.002%, maintaining excellent transmission reliability; the packet loss rate of medium-priority data streams is between 0.002% and 0.003%, which is also at a relatively good level; and the packet loss rate of low-priority data streams is 0.09%, which is within an acceptable range.

[0106] 2. Six data streams are configured: 10ms signal, 20ms signal, Camera signal, Radar signal, 50ms signal, and Lidar signal, divided into high, medium, and low priorities. Among them, the 10ms and 20ms signals are high priority; the Camera and Radar signals are medium priority; and the 50ms and Lidar signals are low priority.

[0107] Four other scheduling methods are introduced—namely: ① GA; ② TS; ③ GA+TS (GA combined with TS); and ④ GA+TS+PC (GA combined with TS and PC)—and compared with the method in Example 1. The minimum end-to-end average delay for each information stream is taken as the evaluation index for optimality. Simulation results show that:

[0108] The method in Example 1 not only achieves low latency for high-priority data streams, but also makes the average latency of other priority information streams lower than that of the other four algorithms, especially for low-priority data streams.

[0109] Example 2

[0110] This embodiment 2 discloses an on-board TSN communication scheduling system for distributed electric drive vehicles, which uses the on-board TSN communication scheduling method for distributed electric drive vehicles in embodiment 1.

[0111] The on-board TSN communication scheduling system for distributed electric drive vehicles includes: a preprocessing module, a data acquisition module, a problem construction module, and a scheduling solution module.

[0112] The preprocessing module constructs the in-vehicle TSN based on the target vehicle's structural composition and extracts all data links. The data acquisition module acquires data appearing in the in-vehicle TSN in real time. N The data streams are combined into a real-time data stream set. The model building module analyzes the information of each data stream in the real-time data stream set, initializes its corresponding time delay offset, and then adds constraints to the real-time data stream set to obtain a constrained model. The scheduling solution module is used to: N < N If 0, then the SMT solver is used to directly solve the constraint model to obtain the scheduling solution; if N ≥ N If the value is 0, the data frame for the constraint model is selected using a sliding window; the SMT solver is used to solve the data selected in the current window to obtain the current solution. Then, the SMT solver is used in conjunction with the current solution to solve the data selected in the next window; if a feasible solution is found, it is updated as the current solution; if no feasible solution is found, the process backtracks to the current window, expands the scope of the current window, and solves again; this process of data selection and solving continues until... N All data streams have been processed, and the current solution is used as the scheduling solution.

[0113] Since this onboard TSN communication scheduling system for distributed electric vehicles uses the onboard TSN communication scheduling method for distributed electric vehicles in Example 1, it has the same effect as Example 1, and will not be repeated here.

[0114] This embodiment 2 also discloses an intelligent vehicle, which is a distributed electric drive vehicle, and uses the on-board TSN communication scheduling method for distributed electric drive vehicles in embodiment 1.

[0115] Example 3

[0116] This embodiment 3 discloses a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the on-board TSN communication scheduling method for distributed electric drive vehicles disclosed in embodiment 1.

[0117] The computer equipment can be either a mobile terminal or a fixed terminal. Examples of the former include mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (such as in-vehicle navigation terminals); examples of the latter include digital TVs and desktop computers.

[0118] This embodiment 3 also discloses a readable storage medium that stores computer program instructions. When the computer program instructions are read and executed by a processor, the steps of the on-board TSN communication scheduling method for distributed electric drive vehicles disclosed in embodiment 1 are performed.

[0119] The readable storage medium may include, but is not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

[0120] This embodiment 3 also discloses a computer program product, including a computer program. When executed by a processor, the computer program implements the steps of the on-board TSN communication scheduling method for distributed electric drive vehicles disclosed in embodiment 1.

[0121] It should be noted that the computer program used to execute the above can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as C or similar languages. The computer program can be executed entirely on the user's computer, partially on the user's computer, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer through any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN).

[0122] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0123] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for scheduling onboard TSN communication for distributed electric vehicles, characterized in that, It includes: Step 1: Construct the vehicle-mounted TSN based on the structural composition of the target vehicle and extract all data links; where the target vehicle is a distributed electric drive vehicle. Step 2: Obtain the real-time information displayed in the vehicle's TSN. N These data streams are combined to form a real-time data stream set. Step 3: Perform information analysis on each data stream in the real-time data stream set, initialize its corresponding time delay offset, and then add constraints to the real-time data stream set to obtain the constraint model. Step four, if N < N If the value is 0, then the SMT solver is used to directly solve the constraint model to obtain the scheduling solution; N 0 indicates a quantity threshold; like N ≥ N If the value is 0, the data frame for the constraint model is selected using a sliding window; the SMT solver is used to solve the data selected in the current window to obtain the current solution. Then, the SMT solver is used in conjunction with the current solution to solve the data selected in the next window; if a feasible solution is found, it is updated as the current solution; if no feasible solution is found, the process backtracks to the current window, expands the scope of the current window, and solves again; this process of data selection and solving continues until... N All data streams have been processed, and the current solution is used as the scheduling solution.

2. The on-board TSN communication scheduling method for distributed electric drive vehicles according to claim 1, characterized in that, In-vehicle TSN includes: M Each terminal node N Each network element node; Among them, a single terminal node corresponds to a single terminal device; a single network element node corresponds to a single central switch; the first m Each terminal node is connected to its corresponding network element node and supports bidirectional communication; m ∈[1, M ];like N If the value is greater than 1, then any two network element nodes can be interconnected and support bidirectional communication; The two endpoints of a single data link are two adjacent nodes in the vehicle-mounted TSN, and there are no other nodes between them.

3. The on-board TSN communication scheduling method for distributed electric drive vehicles according to claim 2, characterized in that, A data stream follows the data link to which it belongs for transmission; A single data stream consists of several data frames; the two endpoints of a single data frame are the same as the two endpoints of its corresponding data link.

4. The on-board TSN communication scheduling method for distributed electric drive vehicles according to claim 3, characterized in that, In step three, information analysis includes: priority classification and data analysis.

5. The on-board TSN communication scheduling method for distributed electric drive vehicles according to claim 4, characterized in that, Step three includes methods for initializing the corresponding delay offset for the data stream: Each data stream in the real-time data stream set is decomposed into several data frames, and a delay offset is initialized for each data frame; Specifically, for a given data frame, the delay offset of its starting point is initialized, and then the delay offset of its ending point is initialized based on the data information transmitted in the data frame and the transmission speed.

6. The on-board TSN communication scheduling method for distributed electric drive vehicles according to claim 4, characterized in that, The constraints include: conflict-free constraints, path-dependent constraints, switch memory constraints, end-to-end transmission constraints, and application-level constraints. Among them, the conflict-free constraint is used to ensure that different data streams transmitted in the same data link do not conflict in terms of time; Path dependency constraints are used to ensure that the transmission delay of a single data frame from its origin to its destination does not exceed a preset maximum frame delay. Switch memory constraints are used to: ensure that the cache on the network element node does not exceed the memory limit of the central switch; End-to-end transmission constraints are used to ensure that the overall transmission delay of a single data stream from its origin to its destination does not exceed a preset maximum stream delay. Application-level constraints are used to ensure that data streams are transmitted in descending order of priority.

7. The on-board TSN communication scheduling method for distributed electric drive vehicles according to claim 6, characterized in that, The expression for a conflict-free constraint is: ； In the formula, v k , v l This indicates two different nodes in the vehicle's TSN; Indicates from v k arrive v l Data links; L This represents the set of all data links in the vehicle-mounted TSN; f i , f j This represents two distinct data streams within a set of real-time data streams; F Represents a collection of real-time data streams; a , b All are period multiple counters; LCM ( F . period () represents the least common multiple of the transmission cycles of all data streams; f i . period express f i The transmission period; f j . period express f j The transmission period; express f i exist Transmission instances on; offset Indicates the transmission start delay offset; The expression for the path dependency constraint is: ； In the formula, vl Indicates a data frame; VL This represents the set of all data frames in the real-time data stream. p i express f i The transmission path; , express p i Two adjacent data frames; max ( hopdelay () indicates the maximum allowable delay for a single hop; The expression for the switch memory constraint is: ； In the formula, membound Indicates the maximum memory buffer time of the switch; The expression for the end-to-end transmission constraint is: ； In the formula, last ( f i )express f i The last data frame in the transmission path; first ( f i )express f i The first data frame in the transmission path; max ( latency ) indicates the maximum permissible end-to-end delay; The expression for the application-level constraint is: ； In the formula, f a , f b This represents two distinct data streams within a set of real-time data streams, and f b The priority is higher than f a Priority; Δ represents the minimum time interval.

8. The on-board TSN communication scheduling method for distributed electric drive vehicles according to claim 5, characterized in that, In step five, the SMT solver uses the z3-solver.

9. A vehicle-mounted TSN communication and dispatching system for distributed electric drive vehicles, characterized in that, It uses the on-board TSN communication scheduling method for distributed electric drive vehicles as described in any one of claims 1-8; The on-board TSN communication and scheduling system for distributed electric drive vehicles includes: The preprocessing module is used to construct the vehicle TSN based on the structural composition of the target vehicle and extract all data links; The data acquisition module is used to acquire data that appears in the vehicle's TSN in real time. N These data streams are combined to form a real-time data stream set. The model building module is used to analyze the information of each data stream in the real-time data stream set, initialize its corresponding time delay offset, and then add constraints to the real-time data stream set to obtain a constrained model. The scheduling and solving module is used for: if N < N If 0, then the SMT solver is used to directly solve the constraint model to obtain the scheduling solution; if N ≥ N If the value is 0, the data frame for the constraint model is selected using a sliding window; the SMT solver is used to solve the data selected in the current window to obtain the current solution. Then, the SMT solver is used in conjunction with the current solution to solve the data selected in the next window; if a feasible solution is found, it is updated as the current solution; if no feasible solution is found, the process backtracks to the current window, expands the scope of the current window, and solves again; this process of data selection and solving continues until... N All data streams have been processed, and the current solution is used as the scheduling solution.

10. A computer program product, characterized in that, It includes a computer program; when the computer program is executed by a processor, it implements the steps of the on-board TSN communication scheduling method for distributed electric drive vehicles as described in any one of claims 1-8.

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