TSN management method, system and terminal based on vehicle state

Abstract

The invention relates to a TSN management method, system and terminal based on a vehicle state, and relates to the technical field of industrial Internet, and the method comprises the steps: obtaining a vehicle operation state and a network flow change condition of a user terminal; sensing and judging a current driving level mode according to the running state of the vehicle; determining a current load state according to the current driving level mode; determining a switch configuration jump threshold according to the current load state; determining a traffic change protection time band according to a switch configuration jump threshold; and inputting the network traffic change condition and the traffic change protection time band into a real-time external SDN controller to analyze and determine a traffic configuration strategy, and outputting the traffic configuration strategy to a preset TSN switch to perform adaptive adjustment of the traffic change. The method and the device have the effect of efficiently allocating and scheduling the network resources in different scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet, and in particular to a TSN management method, system and terminal based on vehicle status. Background Art

[0002] Industrial Internet technology is the product of the deep integration of new-generation information and communication technology and industrial economy. It realizes the interconnection of industrial equipment, systems and services through Internet technology, and is widely used in manufacturing, energy, logistics, mining and power industries.

[0003] During the operation of the autonomous driving system, the Industrial Internet is generally used to process and analyze the massive amounts of data generated, such as sensor data, vehicle control instructions, camera data, etc. Since the data generated by the operation of the autonomous driving system needs to be transmitted reliably in a very short time, the Industrial Internet generally uses the Time Sensitive Network (TSN) to quickly and accurately transmit information such as traffic events, road conditions, and vehicle control instructions perceived by sensors.

[0004] Since network traffic in autonomous driving is dynamic, the priority and demand of data traffic will change when the vehicle is in different driving scenarios, making it inconvenient to allocate and schedule network resources in a timely manner. Summary of the invention

[0005] In order to efficiently allocate and schedule network resources in different scenarios, the present invention provides a TSN management method, system and terminal based on vehicle status.

[0006] In a first aspect, the present invention provides a TSN management method based on vehicle status, which adopts the following technical solution: A TSN management method based on vehicle status, comprising: Obtain the vehicle operation status and network traffic changes of the user terminal; Sense and determine the current driving level mode based on the vehicle's operating status; Determine the current load state according to the current driving level mode; Determine the switch configuration jump threshold according to the current load status; Determine the flow change protection time band according to the switch configuration jump threshold; Based on the network traffic changes and the traffic change protection time band, the real-time external SDN controller is input to analyze and determine the traffic configuration strategy, and the traffic configuration strategy is output to the preset TSN switch for adaptive adjustment of traffic changes.

[0007] Optionally, the method for determining the switch configuration transition threshold includes: Obtain the vehicle's complete travel flow information and current driver information; Retrieving a first road condition parameter value and a second road condition parameter value based on the vehicle complete trip flow information; According to the corresponding relationship between the current load state and the preset load level value, a load level value corresponding to the current load state is determined; Determine a data frame probability density distribution value according to the load level value, the first road condition parameter value and the second road condition parameter value; Retrieving the driving habit information of the driver based on the current driver information; The jump selection threshold is determined according to the probability density distribution value of the data frame and the driving habit information of the personnel, and the jump selection threshold is used as the switch configuration jump threshold.

[0008] Optionally, a method for determining a probability density distribution value of a data frame includes: Based on a preset data frame probability density distribution value calculation formula, the load level value, the first road condition parameter value and the second road condition parameter value are analyzed and calculated to obtain a data frame probability density distribution value; The calculation formula of the probability density distribution value of the data frame is: f(x,k,θ)=(x k-1 e -x / θ )\[θ k Γ(k)]; x is the load level value; k is the first road condition parameter value; θ is the second road condition parameter value.

[0009] Optionally, a method for determining a transition selection threshold includes: Get the current time point; Based on the current time point, retrieve the historical habit information and the habit ratio value of the same period from the driving habit information of the personnel; According to the correspondence between the historical habit information of the same period and the preset habit influence value of the same period, the habit influence value of the same period corresponding to the historical habit information of the same period is determined; Based on the driving habit information of the personnel, historical common habit information and the proportion of common habits are retrieved; According to the correspondence between the historical common habit information and the preset historical habit influence value, the historical habit influence value corresponding to the historical common habit information is determined; Calculate the difference between the habit proportion value and the common habit proportion value in the same period and use it as the habit proportion deviation value; Determine the comprehensive adjustment value of habits based on the historical habit impact value, the habit impact value of the same period and the habit proportion deviation value; Based on the customary comprehensive adjustment value, the probability density distribution value of the data frame is adjusted to form a jump selection threshold.

[0010] Optionally, the method for determining the habit comprehensive adjustment value includes: Determine whether the habit ratio deviation value is less than a preset ratio deviation reference value; If yes, the habit impact value of the same period is used as the comprehensive habit adjustment value; If not, then according to the correspondence between the habitual proportion deviation value and the preset proportion deviation influence value, determine the proportion deviation influence value corresponding to the habitual proportion deviation value; Calculate the product of the proportion deviation impact value and the historical habit impact value and use it as the historical comprehensive impact value; The sum of the historical comprehensive impact value and the habitual impact value of the same period is calculated and used as the comprehensive impact value of the same period, and the comprehensive impact value of the same period is used as the habitual comprehensive adjustment value.

[0011] Optionally, the method for determining the traffic configuration strategy includes: Retrieve the traffic jump value based on the network traffic change; Determine whether the traffic jump value is consistent with the switch configuration jump threshold; If yes, the preset benchmark scheduling strategy is output and used as the traffic configuration strategy; If not, the difference between the traffic jump value and the switch configuration jump threshold is calculated and used as the jump abnormality value; Retrieve the abnormal duration value based on the jump abnormal value; If and only if the abnormal duration value is greater than the flow change protection time band, the state configuration strategy is called based on the current load state and used as the flow configuration strategy.

[0012] Optionally, the state configuration strategy includes: dynamically adjusting queue priority strategy, dynamically adjusting queue credit value increase / decrease rate strategy, dynamically switching traffic scheduling strategy, dynamically adjusting traffic monitoring and forwarding strategy, and dynamically allocating queue memory strategy.

[0013] Optionally, the steps after using the state configuration policy as the traffic configuration policy are as follows: Determine whether the jump abnormal value is a positive value; If yes, then according to the correspondence between the abnormal jump value and the preset positive feedback scheduling strategy, the positive feedback scheduling strategy corresponding to the abnormal jump value is determined, and the positive feedback scheduling strategy is added to the traffic configuration strategy to form a new traffic configuration strategy; If not, the negative feedback scheduling strategy corresponding to the jump abnormal value is determined according to the correspondence between the jump abnormal value and the preset negative feedback scheduling strategy, and the negative feedback scheduling strategy is added to the traffic configuration strategy to form a new traffic configuration strategy.

[0014] In a second aspect, the present invention provides a TSN management system based on vehicle status, which adopts the following technical solution: A TSN management system based on vehicle status, comprising: The acquisition module is used to obtain the vehicle operation status, network traffic changes, vehicle complete trip traffic information, current driver information and current time point; A memory, used to store a program of the TSN management method based on vehicle status as described in any one of the first aspects; The processor loads and executes the program in the memory.

[0015] In a third aspect, the present invention provides an intelligent terminal, which adopts the following technical solution: An intelligent terminal comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes the vehicle status-based TSN management method as described in any one of the first aspects.

[0016] In summary, the present invention includes at least one of the following beneficial technical effects: 1. By obtaining the vehicle operation status and network traffic changes and judging the current driving level mode, and then determining the current load status, the switch configuration jump threshold is determined according to the current load status, and then the traffic change protection time band is determined. The traffic configuration strategy is determined and output for execution according to the network traffic change and the traffic change protection time band, so as to perform adaptive adjustment of traffic changes, thereby achieving the effect of efficient allocation and scheduling of network resources in different scenarios; 2. By acquiring the vehicle's complete trip flow information and the current driver information and retrieving the first road condition parameter value and the second road condition parameter value, the load level value is determined by querying the current load state and the data frame probability density distribution value is determined with the first road condition parameter value and the second road condition parameter value, the personnel driving habit information is retrieved by the current driver information, and then the jump selection threshold is determined by the data frame probability density distribution value and the personnel driving habit information and used as the switch configuration jump threshold, thereby improving the accuracy of the obtained switch configuration jump threshold; 3. The traffic jump value is retrieved through the network traffic change situation and it is determined whether it is consistent with the switch configuration jump threshold. If and only if it is inconsistent, the jump abnormality value is calculated and the abnormal duration value is retrieved. If and only if the abnormal duration value is greater than the traffic change protection time band, the state configuration strategy is retrieved through the current load state and used as the traffic configuration strategy, thereby improving the accuracy of the obtained traffic configuration strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of a method for TSN management based on vehicle status according to an embodiment of the present application; Figure 2 is a flow chart of a method for determining a switch configuration transition threshold according to an embodiment of the present application; Figure 3 is a flow chart of a method for determining a transition selection threshold value according to an embodiment of the present application; Figure 4 is a flow chart of a method for determining a habit comprehensive adjustment value according to an embodiment of the present application; Figure 5 is a flow chart of a method for determining a traffic configuration strategy according to an embodiment of the present application; Figure 6 It is a flow chart of the method of taking the state configuration strategy as the subsequent step of the traffic configuration strategy in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0019] A TSN management method based on vehicle status obtains the vehicle's operating status, network traffic changes, vehicle complete trip traffic information, current driver information and current time point, thereby analyzing the current load status and driver driving habits, and adaptively adjusting traffic changes, thereby achieving the effect of efficient allocation and scheduling of network resources in different scenarios.

[0020] Reference Figure 1 , an embodiment of the present invention discloses a TSN management method based on vehicle status, which includes: Step S100: Obtain the vehicle operation status and network traffic change of the user terminal.

[0021] Among them, the vehicle operation status refers to the status corresponding to the vehicle operation at the current time. The vehicle operation status is obtained by querying the user terminal. The user terminal refers to the vehicle terminal that matches with the personnel and can receive and send information. The user terminal can obtain the vehicle status in real time. The network traffic change refers to the traffic change of the data generated during the operation of the vehicle's automatic driving system at the current time. The network traffic change is detected and obtained through the preset traffic monitor.

[0022] Step S101: Sense and determine the current driving level mode according to the vehicle operating status.

[0023] Among them, the current driving level mode refers to the level mode in which a person is driving the vehicle. The vehicle automatically senses and switches between different driving level modes based on the external environment and driver behavior. The driving level modes include automatic driving mode, manual intervention driving mode, and static parking mode.

[0024] The vehicle automatically senses and switches between different driving levels through the vehicle's operating status. In the automatic driving mode, the vehicle collects information about the surrounding environment through sensors, cameras, radars and other perception systems, and uses artificial intelligence algorithms to make decisions and automatically control the vehicle's acceleration, braking, steering and parking operations. The driver does not need to actively intervene, and the vehicle can automatically drive according to the predetermined route and real-time road conditions. When the driver thinks it is necessary to take over control, the vehicle switches from automatic driving mode to manual intervention mode. In this mode, the driver actively intervenes and the vehicle is no longer completely controlled by the automatic system. The driver can partially or completely take over the operation of the vehicle. When the vehicle is in the parking state, the engine or electric system is turned off, and all driving and control operations are suspended. The system uses a series of sensors and driver control to decide whether to remain in the automatic driving, manual intervention driving or static parking state.

[0025] Step S102: Determine the current load state according to the current driving level mode.

[0026] Among them, the current load state refers to the load state of the network in the TSN switch preset at the current time. The TSN switch refers to a device used to schedule the data generated during the operation of the autonomous driving system. Different current driving level modes correspond to different current load states, and the current load states include high-pressure load states, medium-pressure load states, and low-pressure load states. When the driving level mode is the autonomous driving mode, the current load state is a high-pressure load state. When the driving level mode is the manual intervention driving mode, the current load state is a medium-pressure load state. When the driving level mode is the static parking mode, the current load state is a low-pressure load state.

[0027] More specifically, when the vehicle is in autonomous driving mode, the load of the TSN network will reach a high-pressure load state. This is because autonomous driving relies on a large amount of real-time sensor data, environmental information processing and control instructions, and these data streams must be completed under extremely low latency and high deterministic transmission conditions. Therefore, the TSN switch needs to prioritize high-priority traffic related to driving safety under high-pressure load conditions to ensure that all critical data can be delivered and processed in real time. When the vehicle enters the manual intervention driving mode, the pressure on the TSN network will be reduced relative to the autonomous driving mode, but it is still in a medium-pressure load state. At this time, the vehicle's intelligent system is still running in the background, continuing to provide functions such as assisted driving, warning and environmental perception. The TSN switch needs to handle more complex network traffic, including both real-time data streams of the driving assistance system and data generated by some driver operations. When the vehicle is in a static parking state, the TSN network is in a low-pressure load state. In this state, the vehicle no longer performs real-time driving operations, the real-time data traffic of the perception system and the control system is greatly reduced, the network traffic is less, and the switch resources can focus on processing lower-priority data transmission tasks, such as system updates and in-vehicle entertainment system data transmission.

[0028] By sensing and determining the driving level mode through the vehicle's operating status and determining the current load status of the switch during operation in the TSN network architecture, the system can dynamically adjust the load configuration of the switch through real-time identification of the driving mode, ensuring that key data streams are processed first in high-risk scenarios, thereby optimizing the use of network resources, reducing latency, and significantly improving the safety and reliability of the vehicle.

[0029] Step S103: Determine the switch configuration transition threshold according to the current load status.

[0030] The switch configuration change threshold refers to the threshold corresponding to the change in the allowed traffic volume configured by the TSN switch, and different current load states correspond to different switch configuration change thresholds. The specific steps for determining the switch configuration change threshold refer to steps S200 to S205.

[0031] Step S104: Determine the flow change protection time band according to the switch configuration jump threshold.

[0032] The flow change protection time band refers to the time duration value corresponding to the allowed flow change. Different switch configuration jump thresholds correspond to different flow change protection time bands. The flow change protection time band is determined by querying the switch configuration jump threshold, which is convenient for subsequent use. And through the flow change protection time band, the mode jitter caused by the sudden change of flow exceeding the threshold range is reduced in the subsequent analysis, which effectively prevents the vehicle driving mode jitter caused by inconspicuous data flow mutations such as system failures, thereby improving safety.

[0033] Furthermore, in order to improve the accuracy of the acquired flow change protection time band, the method for determining the flow change protection time band includes: Step S1041: determining a transition threshold reference time value corresponding to the switch configuration transition threshold according to a correspondence between the switch configuration transition threshold and a preset transition threshold reference time value.

[0034] The jump threshold reference time value refers to the reference time value that can be allowed to change according to the switch configuration jump threshold. Different switch configuration jump thresholds correspond to different jump threshold reference time values. The jump threshold reference time value is obtained by querying from a database storing different switch configuration jump thresholds and corresponding jump threshold reference time values. The database is obtained after pre-input. The jump threshold reference time value is determined by querying the switch configuration jump threshold, which is convenient for subsequent use.

[0035] Step S1042: determining a load state influence value corresponding to the current load state according to a correspondence between the current load state and a preset load state influence value.

[0036] The load state impact value refers to the impact degree value of the load state on the time. Different current load states correspond to different load state impact values. The load state impact value is obtained by querying from a database storing different current load states and corresponding load state impact values. The database is obtained after pre-input. The load state impact value is determined by querying the current load state to facilitate subsequent use.

[0037] Step S1043: Calculate the product value between the transition threshold reference time value and the load state impact value and use it as the transition threshold time adjustment value.

[0038] Among them, the jump threshold time adjustment value refers to the time value after adjusting the time that the jump threshold can allow to change according to the current state. The product value between the jump threshold reference time value and the load state impact value is calculated and used as the jump threshold time adjustment value for convenience in subsequent use.

[0039] Step S1044: Get the current time point.

[0040] The current time point refers to the time point corresponding to the current time, and the current time point is obtained by querying a database of the current time.

[0041] Step S1045: Determine the current time reference value corresponding to the current time point according to the correspondence between the current time point and the preset current time reference value.

[0042] The current time reference value refers to the reference time value corresponding to the allowable change of the jump threshold according to the current time. Different current time points correspond to different current time reference values. The current time reference value is obtained by querying from a database storing different current time points and corresponding current time reference values. The database is obtained after pre-input. The current time reference value is determined by querying the current time point to facilitate subsequent use.

[0043] Step S1046: Determine whether the transition threshold time adjustment value is greater than the current time reference value. If yes, execute step S1047; if no, execute step S1048.

[0044] Wherein, whether the time is too long is determined by judging whether the jump threshold time adjustment value is greater than the current time reference value.

[0045] Step S1047: Use the current time reference value as the flow change protection time band.

[0046] When the jump threshold time adjustment value is greater than the current time reference value, it means that the time is too long, so the current time reference value is used as the flow change protection time band, thereby improving the accuracy of the obtained flow change protection time band.

[0047] Step S1048: Determine the current time adjustment value corresponding to the current time reference value according to the correspondence between the current time reference value and the preset current time adjustment value.

[0048] Among them, the current time adjustment value refers to the adjustment value adjusted according to the current time. Different current time reference values ​​correspond to different current time adjustment values. The current time adjustment value is obtained by querying from a database storing the current time adjustment value, and the database is obtained after pre-input.

[0049] When the jump threshold time adjustment value is not greater than the current time reference value, it means that the time is not too long at this time, so the current time adjustment value is determined by querying the current time reference value to facilitate subsequent use.

[0050] Step S1049: Calculate the product value between the jump threshold time adjustment value and the current time adjustment value and use it as the flow change protection time band.

[0051] The accuracy of the acquired flow change protection time band is improved by calculating the product value between the jump threshold time adjustment value and the current time adjustment value and using it as the flow change protection time band.

[0052] Step S105: Based on the network traffic change situation and the traffic change protection time band, the real-time external SDN controller is input to analyze and determine the traffic configuration strategy, and the traffic configuration strategy is output to the preset TSN switch to perform adaptive adjustment of the traffic change.

[0053] Among them, the real-time external SDN controller refers to a controller that configures traffic in real time. By inputting the network traffic changes and the traffic change protection time band into the real-time external SDN controller, the traffic configuration strategy is analyzed and determined, and the traffic configuration strategy is output to the preset TSN switch for adaptive adjustment of traffic changes, thereby achieving the effect of efficient allocation and scheduling of network resources in different scenarios.

[0054] exist Figure 1 In step S103, in order to further ensure the rationality of the switch configuration jump threshold, it is necessary to further analyze and calculate the switch configuration jump threshold. Figure 2 The steps shown are explained in detail.

[0055] Reference Figure 2 The method for determining the switch configuration jump threshold includes the following steps: Step S200: Obtain the vehicle's complete travel flow information and current driver information.

[0056] Among them, the vehicle complete trip flow information refers to the flow information corresponding to the complete trip of the vehicle during operation, and the vehicle complete trip flow information is obtained by querying the user terminal. The current driver information refers to the information of the person driving the vehicle at the current time, and the current driver information is obtained by querying the user terminal.

[0057] Step S201: Retrieve a first road condition parameter value and a second road condition parameter value based on the vehicle complete travel flow information.

[0058] Among them, the first road condition parameter value refers to the flow parameter corresponding to normal road conditions, and the second road condition parameter value refers to the flow parameter corresponding to abnormal road conditions such as congestion. The first road condition parameter value and the second road condition parameter value are retrieved through the vehicle's complete trip flow information to facilitate subsequent use.

[0059] Step S202: determining a load level value corresponding to the current load state according to a corresponding relationship between the current load state and a preset load level value.

[0060] The load level value refers to the total value of the flow of data processed under the load state at the current time. Different load states correspond to different load level values. The load level value is obtained by querying from a database storing different load states and corresponding load level values. The database is obtained after pre-input. The load level value is determined by querying the current load state to facilitate subsequent use.

[0061] Step S203: determining a data frame probability density distribution value according to the load level value, the first road condition parameter value and the second road condition parameter value.

[0062] Among them, the data frame probability density distribution value refers to the numerical value corresponding to the probability density distribution of the current load level in the data frame. By analyzing the load level value, the first road condition parameter value and the second road condition parameter value, the data frame probability density distribution value is determined to facilitate subsequent use.

[0063] The method for determining the probability density distribution value of the data frame includes: Based on a preset data frame probability density distribution value calculation formula, the load level value, the first road condition parameter value and the second road condition parameter value are analyzed and calculated to obtain a data frame probability density distribution value.

[0064] The data frame probability density distribution value calculation formula refers to the formula used to calculate the data frame probability density distribution value, and the data frame probability density distribution value calculation formula is obtained after pre-input. The data frame probability density distribution value calculation formula is: f(x, k, θ) = (x k-1 e -x / θ )\[θ k Γ(k)]; x is the load level value; k is the first road condition parameter value; θ is the second road condition parameter value. The load level value, the first road condition parameter value and the second road condition parameter value are analyzed and calculated by the data frame probability density distribution value calculation formula, so as to obtain the data frame probability density distribution value, thereby improving the accuracy of the obtained data frame probability density distribution value.

[0065] Step S204: Retrieve the driving habit information based on the current driver information.

[0066] The driving habit information of the person refers to the driving habit information corresponding to the person driving the vehicle at the current time. The driving habit information of the person is retrieved through the current driver information for convenience of subsequent use.

[0067] Step S205: determining a jump selection threshold value according to the probability density distribution value of the data frame and the driving habit information of the personnel, and using the jump selection threshold value as the switch configuration jump threshold value.

[0068] The jump selection threshold refers to the selected value corresponding to the threshold value that allows the flow to jump. The jump selection threshold is determined by analyzing the probability density distribution value of the data frame and the driving habit information of the personnel, and the jump selection threshold is used as the switch configuration jump threshold to improve the accuracy of the obtained switch configuration jump threshold. The specific steps for determining the jump selection threshold refer to steps S300 to S307.

[0069] exist Figure 2 In step S205, in order to further ensure the rationality of the transition selection threshold, it is necessary to further analyze and calculate the transition selection threshold separately, specifically by Figure 3 The steps shown are explained in detail.

[0070] Reference Figure 3 , the method for determining the transition selection threshold includes the following steps: Step S300: Obtain the current time point.

[0071] The current time point refers to the time point corresponding to the current time, and the current time point is obtained by querying a database of the current time.

[0072] Step S301: based on the current time point, retrieve the historical habit information and the habit ratio of the same period from the driving habit information of the personnel.

[0073] Among them, the historical habit information of the same period refers to the habit information of people in the historical period consistent with the current time, the proportion of habits of the same period, and the ratio of the time corresponding to the time when people use habits consistent with the current time to the total time of the historical period. The historical habit information of the same period and the proportion of habits of the same period are retrieved from the driving habit information of the people at the current time point to facilitate subsequent use.

[0074] Step S302: determining the contemporaneous habit influence value corresponding to the contemporaneous historical habit information according to the correspondence between the contemporaneous historical habit information and the preset contemporaneous habit influence value.

[0075] The concurrent habit impact value refers to the impact degree value of the concurrent habit. Different concurrent historical habit information corresponds to different concurrent habit impact values. The concurrent habit impact value is obtained by querying from a database storing different concurrent historical habit information and corresponding concurrent habit impact values. The database is obtained after pre-input. The concurrent habit impact value is determined by querying the concurrent historical habit information to facilitate subsequent use.

[0076] Step S303: Retrieving historical common habit information and common habit ratio values ​​based on the person's driving habit information.

[0077] Among them, the historical common habit information and the proportion of common habits refer to the habit information that accounts for the largest proportion of time used by people in the historical period, and the proportion of common habits refers to the proportion of the usage time of common habits to the time corresponding to the historical period. The historical common habit information and the proportion of common habits are retrieved through the driving habit information of people to facilitate subsequent use.

[0078] Step S304: determining the historical habit influence value corresponding to the historical habit information according to the correspondence between the historical common habit information and the preset historical habit influence value.

[0079] The historical habit impact value refers to the impact degree value of the historical habit. Different historical common habit information corresponds to different historical habit impact values. The historical habit impact value is obtained by querying from a database storing different historical common habit information and corresponding historical habit impact values. The database is obtained after pre-input. The historical habit impact value is determined by querying the historical common habit information to facilitate subsequent use.

[0080] Step S305: Calculate the difference between the habit proportion value in the same period and the common habit proportion value and use it as the habit proportion deviation value.

[0081] The habit proportion deviation value refers to the deviation value corresponding to the deviation between the proportion of habits in the same period and the commonly used habits. The difference between the habit proportion value in the same period and the commonly used habit proportion value is calculated and used as the habit proportion deviation value to facilitate subsequent use.

[0082] Step S306: Determine the comprehensive habit adjustment value according to the historical habit impact value, the concurrent habit impact value and the habit proportion deviation value.

[0083] Among them, the comprehensive adjustment value of habits refers to the adjustment value based on comprehensive adjustments of habits. By analyzing the historical habit impact value, the habit impact value of the same period and the habit proportion deviation value, the comprehensive adjustment value of habits can be determined to facilitate subsequent use.

[0084] Step S307: adjusting the data frame probability density distribution value based on the customary comprehensive adjustment value to form a jump selection threshold.

[0085] The probability density distribution value of the data frame is adjusted by using a customary comprehensive adjustment value to form a jump selection threshold, thereby improving the accuracy of the acquired jump selection threshold.

[0086] exist Figure 3In step S306, in order to further ensure the rationality of the habit comprehensive adjustment value, it is necessary to further analyze and calculate the habit comprehensive adjustment value. Figure 4 The steps shown are explained in detail.

[0087] Reference Figure 4 , the method for determining the habit comprehensive adjustment value includes the following steps: Step S400: Determine whether the habit ratio deviation value is less than a preset ratio deviation reference value. If yes, execute step S401; if no, execute step S402.

[0088] The ratio deviation reference value refers to the maximum deviation value allowed for the ratio when the concurrent habit influence value can be adopted, and the ratio deviation reference value is obtained after pre-input. By judging whether the habit ratio deviation value is less than the preset ratio deviation reference value, it is judged whether the concurrent habit influence value can be directly adopted.

[0089] Step S401: taking the habit influence value of the same period as the habit comprehensive adjustment value.

[0090] Among them, when the habit proportion deviation value is less than the preset proportion deviation benchmark value, it means that the habit influence value of the same period can be directly adopted at this time, so the habit influence value of the same period is used as the habit comprehensive adjustment value, thereby improving the accuracy of the obtained habit comprehensive adjustment value.

[0091] Step S402: determining a proportion deviation influence value corresponding to the habitual proportion deviation value according to a correspondence between the habitual proportion deviation value and a preset proportion deviation influence value.

[0092] Among them, the proportion deviation impact value refers to the impact degree value generated when there is a deviation in the proportion. Different habitual proportion deviation values ​​correspond to different proportion deviation impact values. The proportion deviation impact value is obtained by querying from a database that stores different habitual proportion deviation values ​​and the corresponding proportion deviation impact values. The database is obtained after pre-input.

[0093] When the habit proportion deviation value is not less than the preset proportion deviation benchmark value, it means that the habit impact value of the same period cannot be directly used at this time. Therefore, the proportion deviation impact value is determined by querying the habit proportion deviation value to facilitate subsequent use.

[0094] Step S403: Calculate the product of the proportion deviation impact value and the historical habit impact value and use it as the historical comprehensive impact value.

[0095] Among them, the historical comprehensive impact value refers to the comprehensive impact degree value generated in the historical period. It is calculated by multiplying the proportion deviation impact value and the historical habit impact value and used as the historical comprehensive impact value for subsequent use.

[0096] Step S404: Calculate the sum of the historical comprehensive impact value and the habitual impact value of the same period and use it as the comprehensive impact value of the same period, and use the comprehensive impact value of the same period as the habitual comprehensive adjustment value.

[0097] Among them, the comprehensive impact value of the same period refers to the comprehensive impact degree value generated by the habits of the same period. The sum of the historical comprehensive impact value and the habit impact value of the same period is calculated and used as the comprehensive impact value of the same period, and the comprehensive impact value of the same period is used as the comprehensive adjustment value of habits, thereby improving the accuracy of the obtained comprehensive adjustment value of habits.

[0098] exist Figure 1 In step S105, in order to further ensure the rationality of the traffic configuration strategy, it is necessary to further analyze and calculate the traffic configuration strategy. Figure 5 The steps shown are explained in detail.

[0099] Reference Figure 5 , the method for determining the traffic configuration strategy includes the following steps: Step S500: Retrieve the traffic jump value based on the network traffic change situation.

[0100] The traffic jump value refers to the change value corresponding to a traffic jump. The traffic jump value is retrieved based on the network traffic change to facilitate subsequent use.

[0101] Step S501: Determine whether the traffic jump value is consistent with the switch configuration jump threshold. If yes, execute step S502; if no, execute step S503.

[0102] The flow rate jump value is judged to be consistent with the switch configuration jump threshold value, thereby judging whether the flow rate needs to be adjusted.

[0103] Step S502: Output the preset benchmark scheduling strategy and use it as the traffic configuration strategy.

[0104] The benchmark scheduling strategy refers to a strategy that does not require rescheduling of traffic, and the benchmark scheduling strategy is obtained through pre-input. When the traffic jump value is consistent with the switch configuration jump threshold, it means that no configuration adjustment is required at this time, so the preset benchmark scheduling strategy is output and used as the traffic configuration strategy, thereby improving the accuracy of the obtained traffic configuration strategy.

[0105] Step S503: Calculate the difference between the traffic jump value and the switch configuration jump threshold and use it as the jump abnormality value.

[0106] Among them, the jump abnormal value refers to the abnormal value corresponding to the abnormal traffic jump. When the traffic jump value is inconsistent with the switch configuration jump threshold, it means that configuration adjustment is required at this time. Therefore, the difference between the traffic jump value and the switch configuration jump threshold is calculated and used as the jump abnormal value for subsequent use.

[0107] Step S504: Retrieve the abnormal duration value based on the jump abnormal value.

[0108] Among them, the abnormal duration value refers to the time value that lasts when there is a jump abnormal value. By retrieving and counting the duration corresponding to the jump abnormal value, and then using the time counting result as the abnormal duration value, the accuracy of the acquired abnormal duration value is improved.

[0109] Step S505: If and only if the abnormal duration value is greater than the flow change protection time band, the state configuration strategy is retrieved based on the current load state, and the state configuration strategy is used as the flow configuration strategy.

[0110] Among them, the state configuration strategy refers to the strategy for configuring traffic based on the state. Different current load states correspond to different state configuration strategies. When the abnormal duration value is greater than the traffic change protection time band, it means that configuration is required at this time. Therefore, the state configuration strategy is called according to the current load state, and the state configuration strategy is used as the traffic configuration strategy, thereby improving the accuracy of the obtained traffic configuration strategy.

[0111] State configuration policies include dynamic queue priority adjustment policy, dynamic queue credit value increase / decrease rate policy, dynamic traffic scheduling policy, dynamic traffic monitoring and forwarding policy, and dynamic queue memory allocation policy. Dynamic queue priority adjustment policy refers to a policy for dynamically adjusting queue priority, dynamic queue credit value increase / decrease rate policy refers to a policy for adjusting the increase / decrease rate of queue credit, dynamic traffic scheduling policy refers to a policy for dynamically switching traffic scheduling, dynamic traffic monitoring and forwarding policy refers to a policy for dynamically adjusting traffic monitoring and forwarding, and dynamic queue memory allocation policy refers to a policy for dynamically allocating queue memory.

[0112] Different current load states correspond to different dynamic queue priority adjustment strategies, dynamic queue credit value increase and decrease rate strategies, dynamic traffic scheduling strategies, dynamic traffic monitoring and forwarding strategies, and dynamic queue memory allocation strategies.

[0113] When the current load state is a high pressure load state, the dynamic adjustment of queue priority strategy, the dynamic adjustment of queue credit value increase and decrease rate strategy, the dynamic switching traffic scheduling strategy, the dynamic adjustment of traffic monitoring and forwarding strategy, and the dynamic allocation of queue memory strategy are all strategies corresponding to maintaining a high level of operation. When the current load state is a medium pressure load state, the dynamic adjustment of queue priority strategy, the dynamic adjustment of queue credit value increase and decrease rate strategy, the dynamic switching traffic scheduling strategy, the dynamic adjustment of traffic monitoring and forwarding strategy, and the dynamic allocation of queue memory strategy are all strategies corresponding to maintaining a medium level of operation. When the current load state is a low pressure load state, the dynamic adjustment of queue priority strategy, the dynamic adjustment of queue credit value increase and decrease rate strategy, the dynamic switching traffic scheduling strategy, the dynamic adjustment of traffic monitoring and forwarding strategy, and the dynamic allocation of queue memory strategy are all strategies corresponding to maintaining a low level of operation.

[0114] exist Figure 5 After step S505, in order to further ensure the rationality of the traffic configuration strategy, it is necessary to further analyze and calculate the traffic configuration strategy. Figure 6 The steps shown are explained in detail.

[0115] Reference Figure 6 , the steps after using the state configuration policy as the traffic configuration policy include the following steps: Step S600: Determine whether the abnormal jump value is a positive value. If yes, execute step S601; if no, execute step S602.

[0116] Among them, by judging whether the jump abnormal value is a positive value, it is judged whether positive feedback is needed.

[0117] Step S601: According to the correspondence between the abnormal jump value and the preset positive feedback scheduling strategy, the positive feedback scheduling strategy corresponding to the abnormal jump value is determined, and the positive feedback scheduling strategy is added to the traffic configuration strategy to form a new traffic configuration strategy.

[0118] Among them, the positive feedback scheduling strategy refers to the strategy corresponding to the scheduling of positive feedback on the traffic. Different jump abnormality values ​​correspond to different positive feedback scheduling strategies. The positive feedback scheduling strategy is obtained by querying from a database storing different jump abnormality values ​​and corresponding positive feedback scheduling strategies, and the database is obtained after pre-input. When the jump abnormality value is a positive value, it means that positive feedback is required at this time. Therefore, the positive feedback scheduling strategy corresponding to the jump abnormality value is used, and the positive feedback scheduling strategy is added to the traffic configuration strategy to form a new traffic configuration strategy, thereby improving the accuracy of the acquired traffic configuration strategy, and then optimizing the system's responsiveness to different driving conditions, ensuring the priority processing of key tasks, and reducing the risk of performance fluctuations caused by load mutations, thereby improving overall driving safety.

[0119] Step S602: According to the correspondence between the abnormal jump value and the preset negative feedback scheduling strategy, the negative feedback scheduling strategy corresponding to the abnormal jump value is determined, and the negative feedback scheduling strategy is added to the traffic configuration strategy to form a new traffic configuration strategy.

[0120] Among them, the negative feedback scheduling strategy refers to the strategy corresponding to the scheduling of negative feedback on the traffic. Different jump abnormal values ​​correspond to different negative feedback scheduling strategies. The negative feedback scheduling strategy is obtained by querying from a database storing different jump abnormal values ​​and the corresponding negative feedback scheduling strategies. The database is obtained after pre-input. When the jump abnormal value is negative, it means that negative feedback is required at this time. Therefore, the negative feedback scheduling strategy corresponding to the jump abnormal value is used, and the negative feedback scheduling strategy is added to the traffic configuration strategy to form a new traffic configuration strategy, thereby improving the accuracy of the acquired traffic configuration strategy, and then optimizing the system's responsiveness to different driving conditions, ensuring the priority processing of key tasks, and reducing the risk of performance fluctuations caused by load mutations, thereby improving overall driving safety.

[0121] Based on the same inventive concept, an embodiment of the present invention provides a TSN management system based on vehicle status, including: The acquisition module is used to obtain the vehicle operation status, network traffic changes, vehicle complete trip traffic information, current driver information and current time point; A memory, used to store a program of the TSN management method based on vehicle status as described above; The processor loads and executes the program in the memory.

[0122] Based on the same inventive concept, an embodiment of the present invention provides an intelligent terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes the TSN management method based on the vehicle status as described above.

[0123] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0124] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A TSN management method based on vehicle status, characterized in that: include: Obtain the vehicle operation status and network traffic changes of the user terminal; Sense and determine the current driving level mode based on the vehicle's operating status; Determine the current load state according to the current driving level mode; Determine the switch configuration jump threshold according to the current load status; Determine the flow change protection time band according to the switch configuration jump threshold; Based on the network traffic changes and the traffic change protection time band, the real-time external SDN controller is input to analyze and determine the traffic configuration strategy, and the traffic configuration strategy is output to the preset TSN switch for adaptive adjustment of traffic changes.

2. The TSN management method based on vehicle status according to claim 1, characterized in that: Methods for determining the switch configuration transition threshold include: Obtain the vehicle's complete travel flow information and current driver information; Retrieving a first road condition parameter value and a second road condition parameter value based on the vehicle complete trip flow information; According to the corresponding relationship between the current load state and the preset load level value, a load level value corresponding to the current load state is determined; Determine a data frame probability density distribution value according to the load level value, the first road condition parameter value and the second road condition parameter value; Retrieving the driving habit information of the driver based on the current driver information; The jump selection threshold is determined according to the probability density distribution value of the data frame and the driving habit information of the personnel, and the jump selection threshold is used as the switch configuration jump threshold.

3. The TSN management method based on vehicle status according to claim 2, characterized in that: The method for determining the probability density distribution value of the data frame includes: Based on a preset data frame probability density distribution value calculation formula, the load level value, the first road condition parameter value and the second road condition parameter value are analyzed and calculated to obtain a data frame probability density distribution value; The calculation formula of the probability density distribution value of the data frame is: f(x,k,θ)=(x k-1 e -x / θ )\[θ k Γ(k)]; x is the load level value; k is the first road condition parameter value; θ is the second road condition parameter value.

4. The TSN management method based on vehicle status according to claim 2, characterized in that: Methods for determining the transition selection threshold include: Get the current time point; Based on the current time point, retrieve the historical habit information and the habit ratio value of the same period from the driving habit information of the personnel; According to the correspondence between the historical habit information of the same period and the preset habit influence value of the same period, the habit influence value of the same period corresponding to the historical habit information of the same period is determined; Based on the driving habit information of the personnel, historical common habit information and the proportion of common habits are retrieved; According to the correspondence between the historical common habit information and the preset historical habit influence value, the historical habit influence value corresponding to the historical common habit information is determined; Calculate the difference between the habit proportion value and the common habit proportion value in the same period and use it as the habit proportion deviation value; Determine the comprehensive adjustment value of habits based on the historical habit impact value, the habit impact value of the same period and the habit proportion deviation value; Based on the customary comprehensive adjustment value, the probability density distribution value of the data frame is adjusted to form a jump selection threshold.

5. The TSN management method based on vehicle status according to claim 4, characterized in that: The methods for determining the comprehensive adjustment value of habits include: Determine whether the habit ratio deviation value is less than a preset ratio deviation reference value; If yes, the habit impact value of the same period is used as the comprehensive habit adjustment value; If not, then according to the correspondence between the habitual proportion deviation value and the preset proportion deviation influence value, determine the proportion deviation influence value corresponding to the habitual proportion deviation value; Calculate the product of the proportion deviation impact value and the historical habit impact value and use it as the historical comprehensive impact value; The sum of the historical comprehensive impact value and the habitual impact value of the same period is calculated and used as the comprehensive impact value of the same period, and the comprehensive impact value of the same period is used as the habitual comprehensive adjustment value.

6. The TSN management method based on vehicle status according to claim 1, characterized in that: Methods for determining traffic configuration strategies include: Retrieve the traffic jump value based on the network traffic change; Determine whether the traffic jump value is consistent with the switch configuration jump threshold; If yes, the preset benchmark scheduling strategy is output and used as the traffic configuration strategy; If not, the difference between the traffic jump value and the switch configuration jump threshold is calculated and used as the jump abnormality value; Retrieve the abnormal duration value based on the jump abnormal value; If and only if the abnormal duration value is greater than the flow change protection time band, the state configuration strategy is called based on the current load state and used as the flow configuration strategy.

7. The TSN management method based on vehicle status according to claim 6, characterized in that: The state configuration strategy includes: dynamically adjusting queue priority strategy, dynamically adjusting queue credit value increase and decrease rate strategy, dynamically switching traffic scheduling strategy, dynamically adjusting traffic monitoring and forwarding strategy, and dynamically allocating queue memory strategy.

8. The TSN management method based on vehicle status according to claim 6, characterized in that: It also includes the steps after using the state configuration policy as the traffic configuration policy, as follows: Determine whether the jump abnormal value is a positive value; If yes, then according to the correspondence between the abnormal jump value and the preset positive feedback scheduling strategy, the positive feedback scheduling strategy corresponding to the abnormal jump value is determined, and the positive feedback scheduling strategy is added to the traffic configuration strategy to form a new traffic configuration strategy; If not, the negative feedback scheduling strategy corresponding to the jump abnormal value is determined according to the correspondence between the jump abnormal value and the preset negative feedback scheduling strategy, and the negative feedback scheduling strategy is added to the traffic configuration strategy to form a new traffic configuration strategy.

9. A TSN management system based on vehicle status, characterized in that: include: The acquisition module is used to obtain the vehicle operation status, network traffic changes, vehicle complete trip traffic information, current driver information and current time point; A memory for storing a program of the vehicle status-based TSN management method according to any one of claims 1 to 8; The processor loads and executes the program in the memory.

10. An intelligent terminal, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a computer program which can be loaded by the processor and executes the vehicle status-based TSN management method according to any one of claims 1 to 8.

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