Parameter adjustment method and device for data distribution service, electronic equipment and readable storage medium

Abstract

The invention discloses a parameter adjustment method and device for a data distribution service, electronic equipment and a readable storage medium. The method comprises the steps that the network state of a vehicle-mounted network of a vehicle is obtained in response to the operation stage of the vehicle, and the network state is used for indicating the operation state of the vehicle-mounted network; based on the network state of the vehicle-mounted network, determining an adjustment strategy for adjusting an initial service quality parameter of a target data distribution service in the vehicle, the initial service quality parameter being pre-configured and completed according to a target service type to which the target data distribution service belongs when the vehicle is in a development stage; and adjusting the initial service quality parameter based on an adjustment strategy to obtain a target service quality parameter of the target data distribution service. According to the invention, the technical problem that a fixed service quality parameter configuration strategy is difficult to adapt to the dynamic change of a vehicle-mounted network environment is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a parameter adjustment method, device, electronic device and readable storage medium for a data distribution service. Background Art

[0002] With the rapid development of intelligent connected vehicle technology, the electronic systems within vehicles are becoming increasingly complex. These systems frequently exchange information through in-vehicle networks to enable features such as autonomous driving, remote diagnostics, and online software upgrades. During these communications, Data Distribution Service (DDS) has become a key technology in in-vehicle network communications due to its efficient real-time and reliable distributed data transmission capabilities.

[0003] In current automotive communication architectures, manual configuration of Quality of Service (QoS) parameters for DDS is often required, and once configured, these parameters remain unchanged. However, due to the rapidly changing in-vehicle network environment, static, fixed QoS parameter configurations are difficult to adapt to these dynamic conditions. This limits the real-time, reliability, and security of network communication services, impacting vehicle performance and user experience.

[0004] Currently, no effective solution has been proposed to the technical problem that the above-mentioned fixed service quality parameter configuration strategy is difficult to adapt to the dynamic changes of the vehicle network environment. Summary of the Invention

[0005] Embodiments of the present invention provide a method, device, electronic device, and readable storage medium for adjusting parameters of a data distribution service, to at least solve the technical problem that a fixed service quality parameter configuration strategy is difficult to adapt to the dynamic changes of the in-vehicle network environment.

[0006] According to one aspect of an embodiment of the present invention, a method for adjusting parameters of a data distribution service is provided. The method includes: in response to a vehicle being in an operating phase, obtaining a network status of the vehicle's onboard network, wherein the network status indicates the operating status of the onboard network; based on the network status of the onboard network, determining an adjustment strategy for adjusting initial quality of service parameters of a target data distribution service in the vehicle, wherein the initial quality of service parameters are pre-configured during the vehicle's development phase based on a target service type to which the target data distribution service belongs; and adjusting the initial quality of service parameters based on the adjustment strategy to obtain target quality of service parameters for the target data distribution service.

[0007] Optionally, based on the network status of the on-board network, an adjustment strategy for adjusting the initial service quality parameters of the target data distribution service in the vehicle is determined, including: determining the network status parameters of the on-board network based on the network status of the on-board network; inputting the network status parameters of the on-board network into a deep reinforcement learning model for prediction, and obtaining an adjustment strategy for adjusting the initial service quality parameters of the target data distribution service, wherein the deep reinforcement learning model is obtained by training a dual deep neural network model using network status parameter samples.

[0008] Optionally, the adjustment strategy is at least one of the following: a delay reduction strategy, a reliability improvement strategy, and a strong security strategy, wherein the delay reduction strategy is used to indicate reducing the data transmission delay time of the target data distribution service, the reliability improvement strategy is used to indicate improving the integrity and accuracy of the data transmission of the target data distribution service, and the strong security strategy is used to indicate strengthening the security of the data transmission of the target data distribution service.

[0009] Optionally, based on the adjustment strategy, the initial service quality parameters are adjusted to obtain the target service quality parameters of the target data distribution service, including: based on the adjustment strategy, determining the adjustment operation required to be performed when adjusting the initial service quality parameters; according to the adjustment operation, adjusting the initial service quality parameters to obtain the target service quality parameters of the target data distribution service.

[0010] Optionally, based on the adjustment strategy, the adjustment operation required to be performed when adjusting the initial service quality parameters is determined, including: based on the adjustment strategy, from a pre-set mapping relationship table of adjustment strategies and adjustment operations, determining the adjustment strategy and the corresponding target adjustment operation; determining the target adjustment operation as the adjustment operation required to be performed on the initial service quality parameters.

[0011] Optionally, the method also includes: determining an initial configuration strategy for configuring the service quality parameters of the target data distribution service based on the target service type to which the target data distribution service belongs; and configuring the service quality parameters of the target data distribution service based on the initial configuration strategy to obtain the initial service quality parameters of the target data distribution service.

[0012] Optionally, based on the target service type to which the target data distribution service belongs, an initial configuration strategy for configuring the service quality parameters of the target data distribution service is determined, including: based on the target service type, obtaining the initial configuration strategy corresponding to the target data distribution service from a preset mapping relationship table of service types and parameter configuration strategies, wherein the preset mapping relationship table of service types and parameter configuration strategies includes mapping relationships between multiple service types and multiple initial configuration strategies, and there is a one-to-one correspondence between the multiple service types and the multiple initial configuration strategies.

[0013] Optionally, the method also includes: obtaining service characteristic parameters of the target data distribution service, wherein the service characteristic parameters are used to at least represent the data transmission rate requirements and data transmission security requirements of the target data distribution service; based on the service characteristic parameters, determining the target service type to which the target data distribution service belongs, wherein the target service type is used to indicate the service attributes of the target data distribution service.

[0014] Optionally, the method also includes: determining the target service type to which the target data distribution service belongs based on the service characteristic parameters, including: inputting the service characteristic parameters into a ternary classification model for classification to obtain the target service type corresponding to the target data distribution service, wherein the ternary classification model includes a mapping relationship between multiple service types and the constraints of the service characteristic parameters, and the constraints of the service characteristic parameters are used to limit the threshold conditions that the service characteristic parameters need to meet.

[0015] Optionally, the multiple service types include at least a safety service type, a real-time service type, and a reliability service type, wherein the data distribution service of the safety service type is used to indicate services associated with the functional safety of the vehicle, the data distribution service of the real-time service type is used to indicate services with a time delay in data transmission lower than a preset time threshold, and the data distribution service of the reliability service type is used to indicate services with data integrity requirements higher than a preset threshold.

[0016] According to another aspect of an embodiment of the present invention, a parameter adjustment device for a data distribution service is also provided, including: an acquisition unit for acquiring the network status of the vehicle's on-board network in response to the vehicle being in an operating stage, wherein the network status is used to indicate the operating status of the on-board network; a determination unit for determining an adjustment strategy for adjusting the initial service quality parameters of the target data distribution service in the vehicle based on the network status of the on-board network, wherein the initial service quality parameters are pre-configured according to the target service type to which the target data distribution service belongs when the vehicle is in a development stage; and an adjustment unit for adjusting the initial service quality parameters based on the adjustment strategy to obtain the target service quality parameters of the target data distribution service.

[0017] According to another aspect of an embodiment of the present invention, an electronic device is provided, including: a memory storing an executable program; and a processor for running the program, wherein the parameter adjustment method for the data distribution service in each embodiment of the present invention is executed when the program is running.

[0018] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the parameter adjustment method of the data distribution service in each embodiment of the present invention.

[0019] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the parameter adjustment method of the data distribution service in each embodiment of the present invention is implemented.

[0020] According to another aspect of an embodiment of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the parameter adjustment method of the data distribution service in each embodiment of the present invention is implemented.

[0021] According to another aspect of an embodiment of the present invention, a computer program is provided. When the computer program is executed by a processor, the parameter adjustment method of the data distribution service in each embodiment of the present invention is implemented.

[0022] According to another aspect of the embodiments of the present invention, a vehicle is provided. The vehicle is used to execute the parameter adjustment method for the data distribution service in the embodiments of the present invention.

[0023] In an embodiment of the present invention, in response to a vehicle being in an operating stage, a network status of the vehicle's on-board network is obtained, wherein the network status is used to indicate the operating status of the on-board network; based on the network status of the on-board network, an adjustment strategy for adjusting initial service quality parameters of a target data distribution service in the vehicle is determined, wherein the initial service quality parameters are pre-configured according to a target service type to which the target data distribution service belongs when the vehicle is in a development stage; based on the adjustment strategy, the initial service quality parameters are adjusted to obtain target service quality parameters of the target data distribution service. That is to say, in an embodiment of the present invention, during the vehicle development stage, the service quality parameters of the data distribution service are initially configured according to the service type to which the data distribution service belongs. After obtaining the initial service quality parameters of the data distribution service, during the vehicle operation stage, the initial service quality parameters can also be adjusted according to the vehicle's on-board network status. That is, the service quality parameters of the data distribution service are not fixed, but can be adjusted in real time according to the on-board network environment. This intelligent adjustment capability significantly enhances the adaptability and robustness of the vehicle communication system, and can maintain the stability and efficiency of the communication service even under harsh network conditions, thereby achieving the technical effect of dynamically adjusting the service quality parameters of the data distribution service in the vehicle, thereby solving the technical problem that the fixed service quality parameter configuration strategy is difficult to adapt to the dynamic changes of the on-board network environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0025] Figure 1 is a flow chart of a method for adjusting parameters of a data distribution service according to an embodiment of the present invention;

[0026] Figure 2 is a flow chart of another method for adjusting parameters of a data distribution service according to an embodiment of the present invention;

[0027] Figure 3 2 is a schematic diagram of a parameter adjustment device for a data distribution service according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] According to an embodiment of the present invention, an embodiment of a parameter adjustment method for a data distribution service is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] Figure 1 FIG. 1 is a flow chart of a method for adjusting parameters of a data distribution service according to an embodiment of the present invention. Figure 1As shown, the method includes the following steps:

[0032] Step S101 : In response to the vehicle being in the running stage, obtaining the network status of the vehicle's in-vehicle network.

[0033] In the technical solution provided in the above step S101 of the present invention, the vehicle is in the operation stage, which is used to indicate that the vehicle has been developed and put into use. In this case, the network status of the vehicle's in-vehicle network can be monitored in real time, wherein the network status of the in-vehicle network is used to indicate the operation status of the in-vehicle network.

[0034] In this embodiment, the purpose of real-time monitoring of the network status of the in-vehicle network is to collect network status parameters of the in-vehicle network to serve as a basis for subsequent adjustment of the service quality parameters of the data distribution service in the vehicle.

[0035] Optionally, the vehicle's CPU load rate can be determined by monitoring the usage of the vehicle's central processing unit (CPU) to determine whether the vehicle's CPU is under load due to computationally intensive tasks. By measuring the current bandwidth usage of the vehicle network, that is, the bandwidth utilization of the vehicle network, it can be determined whether the vehicle network is close to saturation and / or whether there is available bandwidth. By collecting the delay fluctuations of the vehicle network, the stability of the vehicle network and the consistency of data transmission can be evaluated. By recording the average delay of data transmission on the vehicle network over a period of time, it can be used to analyze network transmission delay information.

[0036] In this step, by real-time monitoring of the vehicle network, network status parameters in the vehicle network can be obtained, such as CPU load rate, bandwidth utilization, network delay information and packet loss rate, thereby providing data basis for the dynamic adjustment of service quality parameters of data distribution services in subsequent vehicle network communications.

[0037] Step S102 : determining an adjustment strategy for adjusting initial quality of service parameters of a target data distribution service in a vehicle based on a network status of the vehicle network.

[0038] In the technical solution provided in the above step S102 of the present invention, the target data distribution service is any one of the data distribution services in the vehicle network communication system used to realize functions such as automatic driving, remote diagnosis, online software upgrade, and in-vehicle entertainment. For example, the data distribution service in the vehicle network communication system provides data transmission support for scenarios such as radar data processing, motor control, active suspension adjustment, and multi-sensor information fusion in the vehicle to ensure that the vehicle can make fast and accurate decisions in complex road environments. During remote diagnosis and online software updates, the data distribution service is responsible for transmitting diagnostic data, fault codes, and update instructions. Its reliability and efficiency are directly related to the real-time monitoring of the vehicle's health status and the consistency upgrade of the software system. For the in-vehicle infotainment system and black box data recording function, the data distribution service ensures the entertainment experience of the passengers and the complete recording of the vehicle's driving information through efficient data transmission.

[0039] In this embodiment, an adjustment strategy for adjusting the initial quality of service parameters of a target data distribution service in a vehicle can be determined based on the network status of the in-vehicle network. The initial quality of service parameters are pre-configured based on the service type of the target data distribution service when the vehicle is in the development phase. That is, when the vehicle is in the development phase, an initial configuration strategy for configuring the quality of service parameters of the target data distribution service can be determined based on the service type of the target data distribution service. The quality of service parameters of the target data distribution service are then configured based on the initial configuration strategy to obtain the initial quality of service parameters of the target data distribution service.

[0040] Optionally, after obtaining the network status of the in-vehicle network, the network status parameters of the in-vehicle network can be determined according to the network status of the in-vehicle network. As can be seen from the introduction of step S101 above, the network status parameters may include at least: CPU load rate, bandwidth utilization, network delay information and packet loss rate. Based on this, the network status parameters can be input into a pre-trained deep reinforcement learning model (e.g., a trained dual-depth Q network model) to determine an adjustment strategy for adjusting the initial service quality parameters of the target data distribution service. The deep reinforcement learning model is obtained by pre-training a dual-depth neural network model (i.e., a dual-depth Q network model) using network status parameter samples. That is, the deep reinforcement learning model is a trained dual-depth Q network model. Among them, after receiving the network status parameters of the in-vehicle network, the deep reinforcement learning model can perform the following steps: state space analysis, for example, converting the acquired network status parameters into a state space representation that can be understood by the deep reinforcement learning model; strategy (action) selection, for example, based on the current network status of the in-vehicle network, predicting a series of possible adjustment strategies, that is, adjustment actions on the initial service quality parameters, such as reducing delay, improving reliability, increasing security, etc., among which reducing delay is used to indicate reducing the delay time of data transmission in the target data distribution service, improving reliability is used to indicate improving the reliability of data transmission in the target data distribution service, or increasing security is used to indicate enhancing the security of data transmission in the target data distribution service, etc.

[0041] Optionally, after the adjustment strategy is determined, the adjustment strategy may be used to guide the adjustment of the initial quality of service parameters of the target data distribution service.

[0042] In this step, the intelligent decision-making capabilities of deep reinforcement learning are used to transform complex network states into easy-to-operate parameter adjustment strategies, thereby making the most appropriate optimization decisions based on real-time situations.

[0043] Step S103: Based on the adjustment strategy, the initial service quality parameters are adjusted to obtain target service quality parameters of the target data distribution service.

[0044] In the technical solution provided by the above step S103 of the present invention, after obtaining the adjustment strategy through the above step S102, the initial service quality parameters of the target data distribution service can be dynamically optimized and adjusted according to the adjustment strategy to obtain the target service quality parameters of the target data distribution service.

[0045] In this embodiment, the initial quality of service parameters associated with the target service are modified based on the generated adjustment policy. For example, if the adjustment policy indicates "reducing latency," the value of the data transmission time limit parameter may be reduced, and the priority of data transmission and reception may be adjusted. The adjusted parameter configuration is then applied to the target data distribution service to improve its real-time performance, reliability, and security.

[0046] Optionally, after adjusting the initial QoS parameters of the target data distribution service, the in-vehicle network status can be monitored to verify that the adjustments have achieved the desired results. If the in-vehicle network environment changes again, the target QoS parameters can be adjusted again according to the above steps. In other words, the process from steps S101 to S103 can be repeated to form a closed-loop optimization mechanism for the QoS parameters of the data distribution service.

[0047] Through the above steps S101 to S103, during the vehicle development stage, the service quality parameters of the data distribution service are initially configured according to the service type to which the data distribution service belongs. After obtaining the initial service quality parameters of the data distribution service, during the vehicle operation stage, the initial service quality parameters can also be adjusted according to the vehicle's on-board network status. That is, the service quality parameters of the data distribution service are not fixed, but can be adjusted in real time according to the on-board network environment. This intelligent adjustment capability significantly enhances the adaptability and robustness of the vehicle communication system, and can maintain the stability and efficiency of the communication service even under harsh network conditions, thereby achieving the technical effect of dynamically adjusting the service quality parameters of the data distribution service in the vehicle, thereby solving the technical problem that the fixed service quality parameter configuration strategy is difficult to adapt to the dynamic changes of the on-board network environment.

[0048] The above method of this embodiment is further introduced below.

[0049] As an optional implementation, step S102, based on the network status of the vehicle network, determines an adjustment strategy for adjusting the initial service quality parameters of the target data distribution service in the vehicle, including: determining the network status parameters of the vehicle network based on the network status of the vehicle network; inputting the network status parameters of the vehicle network into a deep reinforcement learning model for prediction, and obtaining an adjustment strategy for adjusting the initial service quality parameters of the target data distribution service, wherein the deep reinforcement learning model is obtained by training a dual deep neural network model using network status parameter samples.

[0050] In this embodiment, after determining the network status of the in-vehicle network, network status parameters of the in-vehicle network can be determined based on the network status of the in-vehicle network. The network status parameters of the in-vehicle network may include at least: CPU load, bandwidth utilization, network latency, and packet loss rate. CPU load reflects the central processing unit's busyness and is used to assess the remaining headroom for data processing; bandwidth utilization measures network link occupancy and reveals transmission capacity bottlenecks; network latency represents the average time it takes for a data packet to be sent and received, which is crucial for real-time communication; and packet loss rate reflects the reliability of data transmission; a high packet loss rate indicates poor communication quality.

[0051] Optionally, after obtaining the network status parameters of the in-vehicle network, the network status parameters of the in-vehicle network may be input into a deep reinforcement learning model for prediction to obtain an adjustment strategy for adjusting the initial service quality parameters of the target data distribution service.

[0052] Optionally, the deep reinforcement learning model is obtained by training a dual deep neural network model using network state parameter samples. The dual deep neural network model can also be called a double deep Q-network model (Double Deep Q-Network, abbreviated as DDQN). The double deep Q-network model consists of two neural network models, namely a main network and a target network. The main network is used for real-time decision-making, and the target network is used for stable prediction of future rewards. The network parameters of the target network are fixed.

[0053] Optionally, when training the dual-depth Q network model using network state parameter samples, the main network repeatedly tests different adjustment strategies (e.g., reducing latency, improving reliability, increasing security, etc.) and receives rewards from the target network feedback, thereby learning to determine the adjustment strategy that best matches the changed network state when the network state changes. The network state parameter samples can be derived from historical network state parameters or manually designed, and can include parameters such as CPU load, bandwidth utilization, network delay, and packet loss rate.

[0054] Optionally, the model parameters of the dual-depth Q network model can be initialized first, and then a network state space including CPU load, bandwidth utilization, network delay and packet loss rate is defined, as well as an action space including reducing delay, improving reliability and enhancing security. The network state space is used to indicate the set of network state parameters that the dual-depth Q network model can receive at a certain moment, and the action space is used to indicate the specific set of adjustment strategies that the dual-depth Q network model can select. Among them, the adjustment strategy set may include three types of adjustment actions, such as reducing latency, improving reliability, and enhancing security. Among them, reducing latency may include: reducing the historical cache depth of data, increasing the priority of data transmission, or adjusting the data transmission deadline parameter; improving reliability may include: adjusting the reliability parameter from a best effort (BEST_EFFORT) strategy to a reliable transmission (RELIABLE) strategy, that is, during data transmission, the mode is changed from not guaranteeing that the data will reach the destination to ensuring that the data can be reliably delivered to the subscriber. Even if the data needs to be retransmitted, the final transmission success must be guaranteed, thereby improving the stability of data transmission; enhancing security may include: setting an exclusive ownership strategy and a topic-based manual activity detection (manual_by_topic) strategy, as well as enabling data encryption and other measures to strengthen the security protection of data transmission.

[0055] Optionally, a reward function can also be defined. This reward function is a crucial component of the Dual Deep Q Network model, guiding model learning. This reward function quantifies the feedback from the network environment after the Dual Deep Q Network model implements a specific adjustment strategy, specifically, its evaluation of "good" and "bad." The reward function is designed based on the following principles: When the model successfully reduces data transmission latency, improves data transmission reliability and security, and enhances system performance through quality of service parameter adjustments, the model receives a positive reward. Conversely, if the quality of service parameters are improperly adjusted, resulting in increased latency, data loss, or reduced security, the model receives a negative reward. The design of the reward function also needs to consider long-term impacts. For example, excessive latency reduction may lead to an inappropriate allocation of network resources, potentially detrimental in the long run. Therefore, the reward calculation must balance short-term benefits with long-term impacts.

[0056] Alternatively, the above reward function can be expressed by the following formula:

[0057] r = 0.6*(1 / delay) + 0.3*reliability coefficient + 0.1*safety level

[0058] Here, r represents the immediate reward, or the feedback score the dual deep Q-network model receives immediately after executing a particular adjustment strategy. For example, r might be positive based on reduced latency, increased reliability, and improved safety, while negative values ​​might be negative. This immediate reward directly informs policy adjustments during model learning. 0.6*(1 / delay) represents the impact of latency on the reward function. Shorter latency results in a larger value for 1 / delay, and therefore contributes more to this component. By assigning a larger weight (0.6) to latency, the model is encouraged to prioritize latency reduction, which is crucial for real-time services in in-vehicle networks. 0.3*Reliability Coefficient: The reliability coefficient reflects the reliability of the service during transmission. For example, based on factors such as packet loss rate, data integrity, and stability, assigning a moderate weight (0.3) to reliability means that the algorithm should not only strive for fast response times but also ensure the quality and stability of data transmission. 0.1*Safety Level: Safety levels are typically measured based on Automotive Safety Integrity Levels (ASILs), which emphasize the safety requirements of the service. Even though the weight is small (0.1), this part cannot be ignored in scenarios related to intelligent driving and vehicle safety, because any slight safety defect may lead to serious consequences.

[0059] Optionally, the reward function is designed to comprehensively consider three key performance indicators (KPIs)—latency, reliability, and safety—through a weighted average. This approach aims to guide the Dual Deep Q Network model in learning how to adapt appropriately to varying service requirements and network conditions. For example, the Dual Deep Q Network model will favor actions that significantly reduce latency, moderately improve reliability, and maintain safety. This design balances the real-time requirements of in-vehicle services with their reliability and safety.

[0060] Optionally, in practical applications, the reward function formula can be fine-tuned based on the needs of specific scenarios. For example, in extreme cases, the safety level might be temporarily weighted higher to prioritize system security. Conversely, in scenarios where the network is relatively stable or the service is extremely latency-sensitive, the latency weight can be appropriately increased. This dynamic adjustment mechanism is a powerful feature of reinforcement learning algorithms, enabling them to more flexibly and intelligently optimize service quality in complex in-vehicle network environments.

[0061] Next, we will take a network state parameter sample as an example to introduce the training process of the dual-depth Q network model.

[0062] Optionally, network state parameter samples can be input into a dual-depth Q-network model. After receiving the current network state parameter samples, the main network uses its internal weights and bias parameters to predict the value (i.e., Q-value) of a set of adjustment strategies (adjustment actions). During the initial adjustment of the vehicular network's quality of service (QoS) parameters, these adjustment strategies may include "reducing data transmission delay," "improving data transmission reliability," or "enhancing data transmission security." Based on the ε-greedy strategy, the main network selects an adjustment strategy to execute. For example, if the adjustment strategy is to reduce data transmission delay, the model will adjust the corresponding initial QoS parameters (e.g., deadline, reducing the depth of historical data storage) to reduce data transmission delay. After executing the selected adjustment strategy, the model observes changes in the vehicular network environment (i.e., changes in the vehicular network's state) and measures new system performance metrics. A reward is calculated based on the improvement (or degradation) in system performance. For example, if the model predicts that the adjustment strategy results in a significant reduction in data transmission delay without a significant increase in packet loss or CPU load, the adjustment will receive a positive reward. Otherwise, a negative reward may be received. The main network stores the experience (s, a, r, s') consisting of the current state, the selected action, the received reward, and the next state in an experience replay buffer. By sampling small batches of experience samples from this buffer, the model can use gradient descent or other optimization algorithms to update its weights, thereby improving its prediction of the action value for a given state.

[0063] Optionally, the main role of the target network is to provide a stable reference point when calculating the expected reward for the next state. The parameter settings of the target network are fixed and are not updated in each training iteration. Instead, the parameters are copied from the main network after a certain number of training batches. When the main network selects an action and executes it, resulting in the next state, the target network is used to predict the Q value of all possible actions in this new state. This is because if the main network that has just undergone a weight update is used for prediction, the Q value will be unstable, which will affect the learning process. The target Q value calculation formula is as follows:

[0064]

[0065] Among them, y i Used to represent the target Q value of the i-th network state parameter sample, r i It is used to represent the immediate reward of the i-th network state parameter sample, γ is the discount factor, which is used to balance the importance of immediate rewards and future rewards, Q′(s′ i , a′) is the target network in state s′ iThe goal of the main network is to make the predicted Q value as close as possible to the target Q value calculated by the target network. This process is achieved by minimizing the mean square error loss function.

[0066] Optionally, through continuous training and adjustment, the main network gradually learns to select a parameter adjustment strategy that maximizes long-term rewards (i.e., system performance improvement) when the network state changes. The existence of the target network ensures the stability of this learning process, avoiding excessive fluctuations in Q values, and allowing the dual deep Q network model to converge to the optimal strategy more robustly.

[0067] Optionally, when the difference between the predicted Q value of the primary network in the dual deep Q network model and the target Q value calculated by the target network is less than a preset threshold, the dual deep Q network model training is determined to be complete. The trained dual deep Q network model can be referred to as a deep reinforcement learning model. Based on this, after obtaining the network state parameters of the in-vehicle network, these network state parameters can be directly input into the deep reinforcement learning model. After analyzing these network state parameters, the model can output an adjustment strategy for adjusting the initial service quality parameters of the target data distribution service.

[0068] Optionally, the adjustment strategy is at least one of the following: a delay reduction strategy, a reliability improvement strategy, and a strong security strategy, wherein the delay reduction strategy is used to indicate reducing the data transmission delay time of the target data distribution service, the reliability improvement strategy is used to indicate improving the integrity and accuracy of the data transmission of the target data distribution service, and the strong security strategy is used to indicate strengthening the security of the data transmission of the target data distribution service.

[0069] As an optional implementation, step S103, based on the adjustment strategy, adjusts the initial service quality parameters to obtain the target service quality parameters of the target data distribution service, including: based on the adjustment strategy, determining the adjustment operation required to be performed when adjusting the initial service quality parameters; according to the adjustment operation, adjusts the initial service quality parameters to obtain the target service quality parameters of the target data distribution service.

[0070] In this embodiment, after determining the adjustment strategy for adjusting the initial service quality parameters of the target data distribution service, the adjustment operation required to adjust the initial service quality parameters can be determined based on the adjustment strategy. The adjustment operation is used to indicate the adjustment action for adjusting the initial service quality parameters, for example, reducing the data transmission time limit by 30%, reducing the storage depth of historical data by 50%, etc.

[0071] Optionally, after the adjustment operation is determined, the initial service quality parameters may be adjusted according to specific adjustment actions included in the adjustment operation, thereby obtaining target service quality parameters of the target data distribution service.

[0072] As an optional implementation method, based on the adjustment strategy, the adjustment operation required to be performed when adjusting the initial service quality parameters is determined, including: based on the adjustment strategy, from a pre-set mapping relationship table of adjustment strategies and adjustment operations, determining the adjustment strategy and the corresponding target adjustment operation; determining the target adjustment operation as the adjustment operation required to be performed on the initial service quality parameters.

[0073] In this embodiment, the mapping relationship table between adjustment policies and adjustment operations includes mapping relationships between adjustment policies and adjustment operations. Therefore, after determining an adjustment policy, the target adjustment operation corresponding to the adjustment policy can be determined from the mapping relationship table based on the adjustment policy, and the target adjustment operation can be determined as the adjustment operation to be performed on the initial quality of service parameters.

[0074] Optionally, Table 1 is a mapping relationship table between adjustment strategies and adjustment operations according to an embodiment of the present invention. As shown in Table 1, adjustment operations corresponding to different adjustment strategies (adjustment actions) are displayed.

[0075] Table 1 Mapping relationship between adjustment strategies and adjustment operations

[0076]

[0077] Optionally, as shown in Table 1, when the "delay reduction" adjustment strategy is selected, the corresponding adjustment operations are: the data transmission deadline (Deadline) is reduced by 30%, the data transmission priority is increased, and the history cache depth (History Depth) is reduced by 50%. That is, in the scenario of data transmission or task execution, the originally set time limit for completing a task or data transmission is shortened by 30%, that is, the transmission of the data packet of the target data distribution service must be completed in a shorter time to reduce the transmission delay. At the same time, the transmission priority of the data packet of the target data distribution service is increased. In addition, by reducing the history cache depth by 50%, the burden of data processing and transmission can be reduced, further accelerating real-time response.

[0078] Optionally, when the "improve reliability" adjustment strategy is selected, the corresponding adjustment operations are: the best effort strategy is adjusted to a reliable strategy, the volatile strategy is adjusted to a local transient strategy, and the historical cache depth is adjusted from 5 to 10. That is, the reliability parameter in the initial service quality parameters is switched from the best effort (BEST_EFFORT) strategy to the reliable (RELIABLE) strategy to ensure that the data in the target data distribution service can be transmitted accurately even when the network conditions are poor. At the same time, the persistence parameter is adjusted from the volatile (VOLATILE) strategy to the local transient (TRANSIENT_LOCAL) strategy to ensure that new subscribers can receive previously sent data, even if the data was sent before the subscriber joined. Finally, the historical cache depth (History Depth) is increased from 5 to 10 to store more historical data, enhance data integrity and system reliability, especially when the network connection is unstable.

[0079] Optionally, when the "Increase Security" adjustment strategy is selected, the corresponding adjustment operations are: enable exclusive ownership and switch to manual control by topic. That is, two important adjustments will be applied. First, it sets the ownership (Ownership) strategy to enable exclusive ownership (EXCLUSIVE), ensuring that the same data topic has only one publisher at any time, preventing data conflicts and unauthorized access, and enhancing data security and consistency. Secondly, the liveliness strategy is switched to topic-based manual liveness detection (MANUAL_BY_TOPIC), requiring publishers to manually maintain their live status, which increases the system's ability to monitor node status, enabling the system to respond to node failures or abnormal behavior in a timely manner, and improving the overall security level.

[0080] Optionally, by dynamically adjusting the initial quality of service parameters of the target data distribution service through the above adjustment operation, the target data distribution service can more flexibly respond to changes in the network environment, thereby improving the real-time performance, reliability and security of the vehicle network system.

[0081] Next, the initial configuration process of the service quality parameters of the target data distribution service is introduced.

[0082] As an optional implementation, the method also includes: determining an initial configuration strategy for configuring the service quality parameters of the target data distribution service based on the target service type to which the target data distribution service belongs; configuring the service quality parameters of the target data distribution service based on the initial configuration strategy to obtain the initial service quality parameters of the target data distribution service.

[0083] In this embodiment, when the vehicle is in the development stage, an initial configuration strategy for configuring the service quality parameters of the target data distribution service can be determined based on the target service type to which the target data distribution service belongs, and then the service quality parameters of the target data distribution service can be configured according to the initial configuration strategy to obtain the initial service quality parameters of the target data distribution service.

[0084] Optionally, the quality of service parameters of the target data distribution service include at least: data frequency, maximum latency, and security level. Based on the quality of service parameters of the target data distribution service, the service type of the target data distribution service can be determined, where the service type is at least one of the following: security-oriented, real-time-oriented, and reliable. For example, a real-time service requires low latency (≤5ms) and high data frequency (≥100Hz), while a security-oriented service emphasizes ASIL level restrictions and low latency (≤50ms).

[0085] Optionally, after determining the target service type to which the target data distribution service belongs, an initial configuration strategy for configuring the service quality parameters of the target data distribution service can be determined based on the target service type to which the target data distribution service belongs, and then based on the initial configuration strategy, the service quality parameters of the target data distribution service can be configured.

[0086] Next, the process of determining an initial configuration strategy for configuring the quality of service parameters of the target data distribution service based on the target service type to which the target data distribution service belongs is introduced.

[0087] As an optional implementation method, based on the target service type to which the target data distribution service belongs, an initial configuration strategy for configuring the service quality parameters of the target data distribution service is determined, including: based on the target service type, obtaining the initial configuration strategy corresponding to the target data distribution service from a preset mapping relationship table of service types and parameter configuration strategies, wherein the preset mapping relationship table of service types and parameter configuration strategies includes mapping relationships between multiple service types and multiple initial configuration strategies, and there is a one-to-one correspondence between the multiple service types and the multiple initial configuration strategies.

[0088] In this embodiment, the preset mapping relationship table of service types and parameter configuration strategies includes initial configuration strategies corresponding to different service types. Based on this, the initial configuration strategy corresponding to the target data distribution service can be obtained through the preset mapping relationship table of service types and parameter configuration strategies.

[0089] Optionally, Table 2 is a mapping relationship table of preset service types and parameter configuration strategies according to an embodiment of the present invention.

[0090] Table 2 Mapping relationship between preset service types and parameter configuration strategies

[0091]

[0092] Alternatively, as shown in Table 2, real-time services focus on the timeliness of data transmission and have extremely strict requirements on latency. They are usually used in highly dynamic environments such as vehicle control systems and sensor data transmission. For example, for real-time service type data distribution services, the corresponding initial configuration strategy is Reliability: BEST_EFFORT, which means that data transmission is done to the best of its ability, and there is no guarantee that the data will be successfully received by the recipient. Data loss is allowed under extreme network conditions. This reduces the resource overhead required for retransmitting data and helps reduce latency. Deadline: 5ms, sets a very short message transmission deadline, that is, the data packet must be delivered within 5 milliseconds, otherwise it will be considered a timeout failure. This is to ensure the real-time nature of the service and to complete data transmission as quickly as possible even under high load conditions.

[0093] Optionally, for a reliable data distribution service, the corresponding initial configuration policy is Reliability: RELIABLE, which means that the target data distribution service will ensure that all data is successfully transmitted, even if data needs to be retransmitted. This ensures data integrity and reduces the risk of data loss. History: KEEP_LAST(5) specifies that each topic will retain the last five data samples. Even if the subscriber is temporarily offline or fails to receive data in time, the subscriber can still obtain the most recent five data items when it comes back online, thus achieving data synchronization and continuity.

[0094] Optionally, for secure data distribution services, the corresponding initial configuration policy is Ownership:EXCLUSIVE, which means that only one publisher can have write access to the same topic at any given time. This avoids data conflicts and ensures the uniqueness and accuracy of control signals. Liveliness:MANUAL_BY_TOPIC: Publishers must proactively and regularly declare their active status for a specific topic, rather than automatically detecting it. This enhances security and control, ensuring the accuracy and controllability of publisher status, especially in scenarios where strict monitoring of participant status is required.

[0095] Optionally, the initial configuration of each service type is designed based on its specific needs to find the right balance between resource usage, data transmission speed, reliability, and security. The flexible combination and application of these three configuration methods can significantly improve the performance and security of vehicle communication systems in different scenarios.

[0096] Optionally, the corresponding initial configuration strategy may be determined from Table 2 above according to the service type to which the target data distribution service belongs.

[0097] Next, the process of determining the target type of the target data distribution service is introduced.

[0098] As an optional implementation, the method also includes: obtaining service characteristic parameters of the target data distribution service, wherein the service characteristic parameters are used to at least represent the data transmission rate requirements and data transmission security requirements of the target data distribution service; based on the service characteristic parameters, determining the target service type to which the target data distribution service belongs, wherein the target service type is used to indicate the service attributes of the target data distribution service.

[0099] In this embodiment, the service characteristic parameters of the target data distribution service include at least data frequency, maximum delay, and security level. The target service type of the target data distribution service can be determined based on the service characteristic parameters of the target data distribution service. The target service type indicates the service attributes of the target data distribution service, which in turn indicate whether the target data distribution service is real-time, reliable, or secure.

[0100] As an optional implementation method, based on the service characteristic parameters, the target service type to which the target data distribution service belongs is determined, including: inputting the service characteristic parameters into a ternary classification model for classification to obtain the target service type corresponding to the target data distribution service, wherein the ternary classification model includes a mapping relationship between multiple service types and the constraints of the service characteristic parameters, and the constraints of the service characteristic parameters are used to limit the threshold conditions that the service characteristic parameters need to meet.

[0101] In this embodiment, a ternary classification model is used to determine the service type corresponding to a data distribution service based on the service characteristic parameters of the data distribution service. The ternary classification model includes a mapping relationship between multiple service types and the constraints of the service characteristic parameters. The multiple service types include at least a safety-related service type, a real-time service type, and a reliability-related service type. The safety-related service type of data distribution service indicates a service associated with the functional safety of the vehicle, the real-time service type of data distribution service indicates a service with a data transmission delay below a preset time threshold, and the reliability-related service type of data distribution service indicates a service with data integrity requirements above a preset threshold.

[0102] Optionally, Table 3 is a mapping relationship table between multiple service types and constraint conditions of service feature parameters according to an embodiment of the present invention.

[0103] Table 3: Mapping relationship between multiple service types and constraints of service characteristic parameters

[0104]

[0105] Optionally, as shown in Table 3, when the latency in the service characteristic parameters is ≤ 50ms and the functional safety is ≥ ASIL-B, the data distribution service corresponding to the service characteristic parameters can be determined to be of a safety type. Application scenarios for this safety type service may include automatic emergency braking, electronic stability control, steer-by-wire, airbag activation, etc., which are not specifically limited here.

[0106] Optionally, when the delay in the service characteristic parameters is ≤5ms and the data frequency is ≥100Hz, the data distribution service corresponding to the service characteristic parameters can be determined to belong to the real-time service type. Application scenarios of real-time services may include radar / lidar data processing, motor (inverter) control, active suspension adjustment, multi-sensor fusion, etc., which are not specifically limited here.

[0107] Optionally, when the latency in the service characteristic parameters is ≤500ms and the packet loss rate is ≤1e-6, the data distribution service corresponding to the service characteristic parameters can be determined to belong to the reliable service type. Application scenarios of reliable services may include: in-vehicle infotainment, remote diagnosis (fault code upload), OTA software updates, in-vehicle black box data recording, etc., which are not specifically limited here.

[0108] In the above steps, clear boundaries are established between different service types. By quantifying service quality parameters such as maximum latency, data frequency, functional safety level, and packet loss rate, service types can be quickly and accurately identified, providing a solid foundation for selecting initial parameter configuration strategies. This classification mechanism not only simplifies the selection of QoS strategies but also helps system designers implement more refined resource allocation and optimization based on the characteristics and requirements of the services, thereby improving the operating efficiency and safety of intelligent connected vehicles.

[0109] The technical solutions of the embodiments of the present invention are described below with reference to preferred implementation methods.

[0110] In the development of modern intelligent connected vehicles, service architecture design is a critical step in ensuring the integrity and efficient collaboration of vehicle functions. As vehicle functionality becomes increasingly complex, the design of service interfaces becomes crucial, especially when these interfaces require real-time data exchange and highly stable communication. Data distribution service protocols are the preferred choice due to their excellent real-time communication capabilities and stable data transmission characteristics.

[0111] In the related art, in actual deployment environments, the impact of the service quality parameter policy configuration of data distribution services on communication efficiency and system reliability cannot be ignored. Traditional service quality parameter configuration relies on the personal experience and manual adjustments of engineers. This method has obvious limitations when faced with the dynamic and complex nature of in-vehicle networks. For example, manual configuration is inefficient. In increasingly complex in-vehicle systems, manually configuring the service quality parameter policies of data distribution services is not only time-consuming but also prone to errors. Especially in scenarios where frequent adjustments are required to adapt to different operating conditions, manual configuration cannot keep up with the rapid pace of change. With the diversification of service types, including sensor data, control instructions, diagnostic information, etc., the existing technology lacks a set of service quality parameter policy hierarchical mechanisms that can effectively classify and manage different service characteristics. As a result, the configuration is not refined enough and it is difficult to meet the performance requirements of specific services. The dynamic characteristics of the in-vehicle network environment, such as bandwidth fluctuations and instantaneous failures of network nodes, require QoS policies to be able to adjust in real time. Fixed parameter configurations are insufficient in such an environment and cannot flexibly meet the high standards of real-time performance and functional safety. Overly conservative configurations may lead to resource waste and network congestion, while overly aggressive configurations may cause data loss or transmission delays, both of which will seriously affect the overall performance and efficiency of the system.

[0112] However, the present invention provides a parameter adjustment method for a data distribution service. When a vehicle is in the development stage, the data distribution service is divided into real-time, reliable, and secure types based on the service characteristic parameters of the data distribution service (such as data frequency, maximum delay, and security level). Then, based on the service type, the initial configuration strategy corresponding to the data distribution service is determined. Then, the service quality parameters of the data distribution service are configured according to the initial configuration strategy to obtain the initial service quality parameters of the data distribution service. When the vehicle is in the operation stage, a dual-depth Q network algorithm is used to monitor the vehicle network status parameters (such as CPU load, bandwidth utilization, network jitter, etc.) in real time. According to the changes in the vehicle network status, an adjustment strategy for adjusting the initial service quality parameters of the data distribution service is determined. Then, the initial service quality parameters are adjusted according to the adjustment strategy to obtain the target service quality parameters of the data distribution service. That is, during the vehicle development stage, the service quality parameters of the data distribution service are initially configured according to the service type of the data distribution service. After obtaining the initial service quality parameters of the data distribution service, during the vehicle operation stage, the initial service quality parameters can also be adjusted according to the vehicle's on-board network status. That is, the service quality parameters of the data distribution service are not fixed, but can be adjusted in real time according to the on-board network environment. This intelligent adjustment capability significantly enhances the adaptability and robustness of the vehicle communication system, and can maintain the stability and efficiency of the communication service even under harsh network conditions, and achieves the technical effect of dynamically adjusting the service quality parameters of the data distribution service in the vehicle, thereby solving the technical problem that the fixed service quality parameter configuration strategy is difficult to adapt to the dynamic changes of the on-board network environment, which is of great significance to improving the overall performance and safety of the vehicle.

[0113] Figure 2 is a flow chart of a parameter adjustment method for another data distribution service according to an embodiment of the present invention. Figure 2 As shown, the method may include the following steps:

[0114] Step S201: input service characteristic parameters of the target data distribution service.

[0115] In this embodiment, the service characteristic parameters of the target data distribution service may include parameters such as data frequency, maximum delay, safety level (ASIL), packet loss rate, etc. The service characteristic parameters of the target data distribution service point can be input into the trained dual deep Q network model.

[0116] Step S202: classification decision.

[0117] In this embodiment, after receiving the service characteristic parameters of the target data distribution service, the dual-depth Q network model can make a classification decision based on the service characteristic parameters of the target data distribution service to determine the target service type to which the target data distribution service belongs.

[0118] Optionally, if the maximum delay in the service characteristic parameters is ≤50ms and the data frequency is ≥100Hz, in this case, it can be determined that the service type of the target data distribution service is real-time, in which case, step S2031 in step S203 can be executed; if the packet loss rate in the service characteristic parameters is ≤1e-6 and the maximum delay is ≤500ms, in this case, it can be determined that the service type of the target data distribution service is reliable, in which case, step S2032 in step S203 can be executed; if the maximum delay in the service characteristic parameters is ≤50ms and ASIL is ≥B, in this case, it can be determined that the service type of the target data distribution service is secure, in which case, step S2033 in step S203 can be executed.

[0119] Step S2031: Determine the initial configuration policy corresponding to the real-time service type.

[0120] In this embodiment, the initial configuration strategy corresponding to the real-time service type can be determined according to the aforementioned Table 2, and then the service parameters of the target data distribution service can be configured to obtain the initial service quality parameters of the target data distribution service.

[0121] Step S2032: Determine the initial configuration strategy corresponding to the reliable service type.

[0122] In this embodiment, the initial configuration strategy corresponding to the reliable service type can be determined according to the aforementioned Table 2, and then the service parameters of the target data distribution service can be configured to obtain the initial service quality parameters of the target data distribution service.

[0123] Step S2033: Determine the initial configuration policy corresponding to the security service type.

[0124] In this embodiment, the initial configuration policy corresponding to the security service type can be determined according to the aforementioned Table 2, and then the service parameters of the target data distribution service can be configured to obtain the initial service quality parameters of the target data distribution service.

[0125] Step S204: monitor the network status of the vehicle network system.

[0126] In this embodiment, the network status of the in-vehicle network system can be monitored in real time.

[0127] Step S205: Determine the adjustment strategy.

[0128] In this embodiment, an adjustment strategy for the initial configuration parameters of the target data distribution service can be determined based on the network status of the in-vehicle network system. If the adjustment strategy is a latency reduction strategy, step S2061 of step S206 is executed; if the adjustment strategy is a reliability improvement strategy, step S2062 of step S206 is executed; if the adjustment strategy is a security enhancement strategy, step S2063 of step S206 is executed.

[0129] Step S2061: Determine an adjustment operation corresponding to the delay reduction adjustment strategy.

[0130] In this embodiment, an adjustment operation corresponding to the delay reduction strategy may be determined.

[0131] Step S2062: Determine an adjustment operation corresponding to a reliable adjustment strategy.

[0132] In this embodiment, an adjustment operation corresponding to the reliability strategy may be determined.

[0133] Step S2063: Determine the adjustment operation corresponding to the security enhancement adjustment strategy.

[0134] In this embodiment, an adjustment operation corresponding to the security enhancement policy may be determined.

[0135] Step S207: performing an adjustment operation.

[0136] In this embodiment, the initial QoS parameters of the target data distribution service can be adjusted based on the adjustment operation determined in step S206 to obtain the target QoS parameters of the target data distribution service. Thereafter, step S204 can be executed to monitor changes in the in-vehicle network environment. If changes occur in the in-vehicle network environment, the target QoS parameters of the target data distribution service can be adjusted again according to steps S204 to S207.

[0137] In the above steps S201 to S207, during the vehicle development stage, the service quality parameters of the data distribution service are initially configured according to the service type to which the data distribution service belongs. After obtaining the initial service quality parameters of the data distribution service, during the vehicle operation stage, the initial service quality parameters can also be adjusted according to the vehicle's on-board network status. That is, the service quality parameters of the data distribution service are not fixed, but can be adjusted in real time according to the on-board network environment. This intelligent adjustment capability significantly enhances the adaptability and robustness of the vehicle communication system, and can maintain the stability and efficiency of the communication service even under harsh network conditions, thereby achieving the technical effect of dynamically adjusting the service quality parameters of the data distribution service in the vehicle, thereby solving the technical problem that the fixed service quality parameter configuration strategy is difficult to adapt to the dynamic changes of the on-board network environment, which is of great significance to improving the overall performance and safety of the vehicle.

[0138] According to an embodiment of the present invention, a parameter adjustment device for a data distribution service is also provided. It should be noted that the parameter adjustment device for a data distribution service can be used to execute the parameter adjustment method for a data distribution service in the embodiment.

[0139] Figure 3 FIG. 1 is a schematic diagram of a parameter adjustment device for a data distribution service according to an embodiment of the present invention. Figure 3 As shown, the parameter adjustment device 300 for the data distribution service may include: an acquisition unit 301 , a determination unit 302 and an adjustment unit 303 .

[0140] An acquiring unit 301 is configured to acquire a network status of an in-vehicle network of the vehicle in response to the vehicle being in an operating phase, wherein the network status is used to indicate an operating status of the in-vehicle network;

[0141] a determining unit 302 for determining, based on a network status of the in-vehicle network, an adjustment strategy for adjusting initial quality of service parameters of a target data distribution service in the vehicle, wherein the initial quality of service parameters are pre-configured according to a target service type of the target data distribution service during a vehicle development phase;

[0142] The adjusting unit 303 is configured to adjust the initial quality of service parameters based on the adjustment strategy to obtain target quality of service parameters of the target data distribution service.

[0143] Optionally, the determination unit 302 also includes: a first determination module, used to determine the network status parameters of the on-board network based on the network status of the on-board network; an input module, used to input the network status parameters of the on-board network into the deep reinforcement learning model for prediction, and obtain an adjustment strategy for adjusting the initial service quality parameters of the target data distribution service, wherein the deep reinforcement learning model is obtained by training a dual deep neural network model using network status parameter samples.

[0144] Optionally, the adjustment unit 303 also includes: a second determination module, used to determine the adjustment operation required to be performed when adjusting the initial service quality parameters based on the adjustment strategy; an adjustment module, used to adjust the initial service quality parameters according to the adjustment operation to obtain the target service quality parameters of the target data distribution service.

[0145] Optionally, the second determination module is further used to: determine the adjustment strategy and the corresponding target adjustment operation from a preset mapping relationship table of adjustment strategies and adjustment operations based on the adjustment strategy; and determine the target adjustment operation as the adjustment operation required to be performed on the initial service quality parameters.

[0146] Optionally, the parameter adjustment device 300 of the data distribution service also includes: a first determination unit, used to determine an initial configuration strategy for configuring the service quality parameters of the target data distribution service based on the target service type to which the target data distribution service belongs; a configuration unit, used to configure the service quality parameters of the target data distribution service based on the initial configuration strategy to obtain the initial service quality parameters of the target data distribution service.

[0147] Optionally, the first determination unit is also used to: based on the target service type, obtain the initial configuration policy corresponding to the target data distribution service from the preset mapping relationship table of service types and parameter configuration policies, wherein the preset mapping relationship table of service types and parameter configuration policies includes mapping relationships between multiple service types and multiple initial configuration policies, and there is a one-to-one correspondence between multiple service types and multiple initial configuration policies.

[0148] Optionally, the parameter adjustment device 300 of the data distribution service also includes: a first acquisition unit, used to obtain service characteristic parameters of the target data distribution service, wherein the service characteristic parameters are used to at least represent the data transmission rate requirements and data transmission security requirements of the target data distribution service; a second determination unit, used to determine the target service type to which the target data distribution service belongs based on the service characteristic parameters, wherein the target service type is used to indicate the service attributes of the target data distribution service.

[0149] Optionally, the third determination unit is also used to: input the service feature parameters into a ternary classification model for classification to obtain a target service type corresponding to the target data distribution service, wherein the ternary classification model includes a mapping relationship between multiple service types and constraints on service feature parameters, and the constraints on service feature parameters are used to limit the threshold conditions that the service feature parameters need to meet.

[0150] In this embodiment, during the vehicle development stage, the service quality parameters of the data distribution service are initially configured according to the service type to which the data distribution service belongs. After obtaining the initial service quality parameters of the data distribution service, during the vehicle operation stage, the initial service quality parameters can also be adjusted according to the vehicle's on-board network status. That is, the service quality parameters of the data distribution service are not fixed, but can be adjusted in real time according to the on-board network environment. This intelligent adjustment capability significantly enhances the adaptability and robustness of the vehicle communication system, and can maintain the stability and efficiency of the communication service even under harsh network conditions, thereby achieving the technical effect of dynamically adjusting the service quality parameters of the data distribution service in the vehicle, thereby solving the technical problem that the fixed service quality parameter configuration strategy is difficult to adapt to the dynamic changes of the on-board network environment, which is of great significance to improving the overall performance and safety of the vehicle.

[0151] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein when the program runs, the parameter adjustment method for the data distribution service in each embodiment of the present invention is executed.

[0152] An embodiment of the present application also provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the parameter adjustment method of the data distribution service in various embodiments of the present invention.

[0153] An embodiment of the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements the parameter adjustment method for the data distribution service in various embodiments of the present invention.

[0154] An embodiment of the present application also provides a computer program product, including a non-volatile computer-readable storage medium, which is used to store a computer program. When the computer program is executed by a processor, it implements the parameter adjustment method of the data distribution service in various embodiments of the present invention.

[0155] An embodiment of the present application further provides a computer program, which, when executed by a processor, implements the parameter adjustment method for the data distribution service in each embodiment of the present invention described above.

[0156] An embodiment of the present application further provides a vehicle, which is used to execute the parameter adjustment method for the data distribution service in various embodiments of the present invention.

[0157] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0158] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0159] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0160] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0161] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0162] If the integrated 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 technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0163] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for adjusting parameters of a data distribution service, characterized in that: include: In response to the vehicle being in the running stage, obtaining a network status of an in-vehicle network of the vehicle, wherein the network status is used to indicate an operating status of the in-vehicle network; determining, based on a network status of the in-vehicle network, an adjustment strategy for adjusting initial quality of service parameters of a target data distribution service in the vehicle, wherein the initial quality of service parameters are pre-configured according to a target service type of the target data distribution service when the vehicle is in a development phase; Based on the adjustment strategy, the initial service quality parameters are adjusted to obtain target service quality parameters of the target data distribution service.

2. The method according to claim 1, characterized in that Determining, based on a network status of the in-vehicle network, an adjustment strategy for adjusting an initial quality of service parameter of a target data distribution service in the vehicle, comprising: Determining a network status parameter of the in-vehicle network based on the network status of the in-vehicle network; The network status parameters of the in-vehicle network are input into a deep reinforcement learning model for prediction to obtain an adjustment strategy for adjusting the initial service quality parameters of the target data distribution service, wherein the deep reinforcement learning model is obtained by training a dual deep neural network model using network status parameter samples.

3. The method according to claim 2, characterized in that The adjustment strategy is at least one of the following: a delay reduction strategy, a reliability improvement strategy, and a strong security strategy, wherein the delay reduction strategy is used to indicate reducing the data transmission delay time of the target data distribution service, the reliability improvement strategy is used to indicate improving the integrity and accuracy of the data transmission of the target data distribution service, and the strong security strategy is used to indicate strengthening the security of the data transmission of the target data distribution service.

4. The method according to claim 1, wherein Adjusting the initial quality of service parameters based on the adjustment strategy to obtain target quality of service parameters of the target data distribution service includes: Determining, based on the adjustment strategy, an adjustment operation required to be performed when adjusting the initial quality of service parameters; According to the adjustment operation, the initial service quality parameters are adjusted to obtain the target service quality parameters of the target data distribution service.

5. The method according to claim 4, characterized in that Determining, based on the adjustment strategy, an adjustment operation to be performed when adjusting the initial quality of service parameter, including: Based on the adjustment strategy, determining the target adjustment operation corresponding to the adjustment strategy from a preset mapping relationship table of adjustment strategies and adjustment operations; The target adjustment operation is determined as the adjustment operation required to be performed on the initial service quality parameter.

6. The method according to claim 1, characterized in that The method further comprises: Determining, based on the target service type to which the target data distribution service belongs, an initial configuration strategy for configuring quality of service parameters of the target data distribution service; Based on the initial configuration strategy, the service quality parameters of the target data distribution service are configured to obtain the initial service quality parameters of the target data distribution service.

7. The method according to claim 6, characterized in that Determining an initial configuration strategy for configuring quality of service parameters of the target data distribution service based on the target service type to which the target data distribution service belongs includes: Based on the target service type, the initial configuration strategy corresponding to the target data distribution service is obtained from the preset mapping relationship table of service types and parameter configuration strategies, wherein the preset mapping relationship table of service types and parameter configuration strategies includes mapping relationships between multiple service types and multiple initial configuration strategies, and the multiple service types and the multiple initial configuration strategies correspond one to one.

8. The method according to claim 1, characterized in that The method further comprises: Acquire service characteristic parameters of the target data distribution service, wherein the service characteristic parameters are used to at least represent a data transmission rate requirement and a data transmission security requirement of the target data distribution service; Based on the service characteristic parameters, the target service type to which the target data distribution service belongs is determined, wherein the target service type is used to indicate a service attribute of the target data distribution service.

9. The method according to claim 8, characterized in that Determining the target service type to which the target data distribution service belongs based on the service characteristic parameters includes: The service characteristic parameters are input into a ternary classification model for classification to obtain the target service type corresponding to the target data distribution service, wherein the ternary classification model includes a mapping relationship between multiple service types and the constraints of the service characteristic parameters, and the constraints of the service characteristic parameters are used to limit the threshold conditions that the service characteristic parameters need to meet.

10. The method according to claim 9, characterized in that The multiple service types include at least a safety service type, a real-time service type and a reliability service type, wherein the data distribution service of the safety service type is used to indicate services associated with the functional safety of the vehicle, the data distribution service of the real-time service type is used to indicate services with a time delay in data transmission lower than a preset time threshold, and the data distribution service of the reliability service type is used to indicate services with data integrity requirements higher than a preset threshold.

11. A parameter adjustment device for a data distribution service, characterized in that: include: an acquiring unit, configured to acquire a network status of an in-vehicle network of the vehicle in response to the vehicle being in an operating stage, wherein the network status is used to indicate an operating status of the in-vehicle network; a determining unit, configured to determine, based on a network status of the in-vehicle network, an adjustment strategy for adjusting initial quality of service parameters of a target data distribution service in the vehicle, wherein the initial quality of service parameters are pre-configured according to a target service type to which the target data distribution service belongs when the vehicle is in a development phase; An adjusting unit is configured to adjust the initial quality of service parameters based on the adjustment strategy to obtain target quality of service parameters of the target data distribution service.

12. An electronic device, characterized in that: include: a memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 10 when running.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 10.

14. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 10.

15. A vehicle, characterized in that: The vehicle is used to perform the method according to any one of claims 1 to 10.

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