High-efficiency vehicle network control system and method based on CAN-FD

Abstract

The application relates to the technical field of vehicle network control, in particular to an efficient vehicle network control system and method based on CAN-FD, which comprises a data transmission optimization module, a bandwidth management module, a signal scheduling control module, an encryption security control module and an abnormality detection module.In the application, the transmission efficiency is improved, invalid data bandwidth occupation is reduced, a priority selection mechanism is called, high-priority signal stable transmission is ensured, bandwidth distribution is adjusted based on flow state and node frequency monitoring, burst flow congestion is relieved, resource distribution is balanced, arbitration priority and bus conflict state are combined, non-emergency signal priority is optimized, the risk of key data obstruction is reduced, encryption and integrity checking are adopted, data safety is ensured, tampering and leakage are prevented, data abnormality scoring and detection are introduced, abnormal data is screened, identification capability is improved, and network security risks are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle network control, and in particular to an efficient vehicle network control system and method based on CAN-FD. BACKGROUND

[0002] The technical field of vehicle network control includes data communication systems within and outside the vehicle, mainly used to ensure information exchange between various electronic control units (ECUs) in the vehicle to support functional execution and safety control of the vehicle. This technical field covers bus-based communication protocols, data transmission mechanisms, real-time control strategies, and network security measures. The current mainstream vehicle network communication protocols include CAN, LIN, FlexRay, and Ethernet. Among them, CAN bus is widely used in power systems, body electronics, and ADAS systems due to its high reliability and low cost advantages. With the increasing complexity of automotive electronic systems.

[0003] Among them, the efficient vehicle network control system based on CAN-FD refers to using CAN-FD protocol to achieve more efficient data communication to meet the needs of high bandwidth, low latency, and high real-time performance of vehicles. This patent topic covers optimization of CAN-FD data frame structure to improve data transmission rate, while using adaptive arbitration mechanism to reduce bus conflicts. For high data flow scenarios, a dynamic load balancing strategy is used to allocate data transmission priority to alleviate network congestion. To ensure data integrity and security, a message integrity check method based on hash function is designed, and encryption authentication technology is combined to improve network anti-interference ability. The system optimizes the clock synchronization accuracy between nodes through a time synchronization mechanism to improve the determinism of data transmission.

[0004] In the prior art, the data frame structure is not fully optimized during data transmission, resulting in a high proportion of padding bytes, increasing bus load, and reducing bandwidth utilization. The data transmission priority mechanism cannot dynamically adapt to network load conditions, causing high-priority signals to be delayed in high-flow environments, affecting the real-time performance of vehicle control. The bandwidth allocation method mainly relies on fixed rules and cannot dynamically adjust according to instantaneous flow changes, easily causing bus congestion under sudden load, reducing the communication efficiency of some nodes, and the signal priority management does not fully consider bus access conflict situations, causing non-critical signals to occupy bandwidth resources and affecting the transmission stability of critical control signals. The data security protection measures are relatively basic and do not combine dynamic keys and data integrity checks, making it difficult to effectively prevent data tampering and illegal access. The abnormality detection method mainly uses fixed threshold judgment and lacks adaptability to complex abnormal behaviors, causing hidden attacks or sudden abnormalities to be not identified in time. SUMMARY

[0005] The application aims to solve the problems in the prior art and provides a high-efficiency vehicle network control system and method based on CAN-FD.

[0006] To achieve the above-mentioned purpose, the application adopts the following technical scheme: a high-efficiency vehicle network control system based on CAN-FD comprises:

[0007] The data transmission optimization module calls vehicle control information data based on CAN-FD data frame information, filters transmission priority threshold, calculates data packet length adjustment parameter, judges padding byte proportion, adjusts data segment rate, and obtains updated data frame parameter;

[0008] The bandwidth management module extracts data bus flow state and node data transmission frequency based on the updated data frame parameter, calculates bandwidth usage mean value, adjusts low-priority node bandwidth proportion, and generates dynamic bandwidth allocation result;

[0009] The signal scheduling control module extracts vehicle network signal priority data based on the dynamic bandwidth allocation result, judges bus access conflict state, adjusts non-emergency signal priority, and generates signal priority adjustment result;

[0010] The encryption security control module extracts encryption key based on the signal priority adjustment result, identifies encryption data and integrity check code, compares data integrity verification value, and generates data integrity test result;

[0011] The abnormality detection module extracts data frame size and sending frequency based on the data integrity test result, calculates data abnormality score, filters abnormality score over-limit data, and generates vehicle network abnormality detection judgment result.

[0012] As a further scheme of the application, the updated data frame parameter comprises data packet length adjustment parameter, padding byte proportion, bit rate adjustment value, and data segment rate setting, the dynamic bandwidth allocation result comprises bandwidth usage mean value, instantaneous flow over-limit node identification, and low-priority node bandwidth proportion setting, the signal priority adjustment result comprises signal priority data, arbitration priority parameter, bus access conflict state identification, and non-emergency signal priority adjustment value, the data integrity test result comprises encryption key parameter, symmetric encryption parameter, encryption data identification, integrity check code value, pre-shared key, and integrity verification result, and the vehicle network abnormality detection judgment result comprises data frame size parameter, sending frequency value, arbitration delay time, data abnormality score value, abnormality detection algorithm, and abnormality score over-limit data identification.

[0013] As a further scheme of the application, the data transmission optimization module comprises:

[0014] The parameter filtering submodule filters data stream type information based on CAN-FD data frame information, including bit rate, data packet length, number of padding bytes, vehicle control signals, status information data, and diagnostic data stream data. It analyzes transmission priority and sets thresholds to filter data streams and obtain the filtered data frame parameters.

[0015] The data packet length adjustment submodule calls the filtered data frame parameters, identifies the proportion of padding bytes, and determines whether it exceeds the limit using the following formula:

[0016]

[0017] The optimized data packet length is calculated.

[0018] Among them, L opt L represents the optimized data packet length. ori P represents the length of the original data packet. k N represents the number of padding bytes in the k-th data frame. eff R represents the number of valid data frames. bit R represents the data segment bit rate. arb The bit rate represents the arbitration phase, and M represents the total number of data frames.

[0019] The data segment rate optimization submodule calls the optimized data packet length, identifies the bit rate ratio, adjusts the data segment rate, and obtains the updated data frame parameters.

[0020] As a further aspect of the present invention, the bandwidth management module includes:

[0021] Based on the updated data frame parameters, the bus traffic monitoring submodule extracts the data bus traffic status, monitors the node data transmission frequency, calculates the average load value of the data bus, analyzes the bandwidth usage, and obtains the data bus traffic distribution.

[0022] The node bandwidth allocation submodule calls the data bus traffic distribution to filter nodes whose instantaneous traffic exceeds the limit, using the following formula:

[0023]

[0024] Calculate the bandwidth adjustment amount for low-priority nodes to obtain the node bandwidth adjustment parameters;

[0025] Among them, B adj B represents the bandwidth adjustment amount for low-priority nodes. avg Use the average value for node bandwidth.

[0026] F node F represents the data transmission frequency of the current node. thr M is the traffic threshold. nodeN is the total number of connections of the node, U curr is the current data bus load;

[0027] The dynamic bandwidth adjustment submodule calls the node bandwidth adjustment parameter, allocates the low-priority node bandwidth proportion according to the node bandwidth adjustment condition, adjusts the data flow priority, and generates a dynamic bandwidth allocation result.

[0028] As a further scheme of the application, the signal scheduling control module comprises:

[0029] The signal priority extraction submodule extracts vehicle network signal priority data based on the dynamic bandwidth allocation result, and counts the initial priority value, signal type, bandwidth occupation ratio and data transmission rate to obtain signal priority interval characteristics.

[0030] The signal arbitration judgment submodule calls the signal priority interval characteristics, judges the bus access conflict state, calculates the signal scheduling conflict coefficient and the bus load balancing coefficient, and obtains the non-emergency signal conflict influence value.

[0031] The signal priority adjustment submodule reassigns the priority based on the non-emergency signal conflict influence value, using the formula:

[0032]

[0033] Adjust the priority of non-emergency signals to generate a signal priority adjustment result.

[0034] Wherein, S adj represents the signal priority adjustment value, S ori represents the initial priority value, C conf represents the priority conflict influence factor, B util represents the bandwidth utilization rate, W bal represents the bus load balancing coefficient, V dyn represents the dynamic bandwidth allocation result, V thr represents the priority adjustment threshold.

[0035] As a further scheme of the application, the encryption security control module comprises:

[0036] The key extraction submodule extracts the encryption key based on the signal priority adjustment result, parses the data frame, filters the matching symmetric encryption parameters, identifies the key validity, and generates the valid encryption key.

[0037] The data encryption identification submodule calls the valid encryption key, parses the data frame structure, identifies the encrypted data and the integrity check code, and uses the formula:

[0038]

[0039] The encrypted data consistency value is compared with the integrity check code to obtain the encrypted data parsing result;

[0040] Among them, C E D represents the data encryption consistency value. enc D represents encrypted data. key K represents the key used to encrypt data. j S represents the weight of the encrypted parameter group. j This represents the data packet security parameters, where m represents the number of encryption parameter groups.

[0041] The integrity verification submodule calls the encrypted data parsing result, parses the pre-shared key, calculates the integrity verification value, filters the data integrity deviation range, and obtains the data integrity verification result.

[0042] As a further aspect of the present invention, the anomaly detection module includes:

[0043] Based on the data integrity verification results, the data error detection and calibration submodule extracts the data frame size, transmission frequency, and arbitration delay, calculates the integrity deviation value, compares it with the integrity benchmark value, and obtains the data integrity deviation analysis results.

[0044] The data anomaly score calculation submodule calls the data integrity deviation analysis results to calculate the anomaly score for each data item, and uses the following formula in conjunction with the integrity deviation:

[0045]

[0046] Obtain the data anomaly score set;

[0047] Among them, D s F represents the score for data anomalies. k F represents the size of the k-th data frame. th L is a baseline value representing the size of a data frame. k T represents the integrity deviation value of the k-th data frame. k A represents the transmission frequency of the k-th data frame. k A represents the arbitration delay of the k-th data frame. th The baseline value representing the arbitration delay, and M representing the total number of data frames;

[0048] The abnormal data filtering submodule calls the data anomaly score set, filters out out-of-limit anomaly score data, marks abnormal data items, extracts abnormal features, and generates vehicle network anomaly detection and judgment results.

[0049] The CAN-FD-based high-efficiency vehicle network control method is executed based on the aforementioned CAN-FD-based high-efficiency vehicle network control system, and includes the following steps:

[0050] S1: based on CAN-FD data frame information, extracting bit rate, data packet length, number of padding bytes, calculating data packet length adjustment parameter, adjusting data segment rate, optimizing data frame transmission, obtaining optimized data frame parameter;

[0051] S2: based on the optimized data frame parameter, extracting data bus flow state, node transmission frequency, average load value, adjusting low priority node bandwidth, optimizing high priority node transmission, obtaining dynamic bandwidth allocation result;

[0052] S3: based on the dynamic bandwidth allocation result, calling signal arbitration priority, analyzing signal access conflict, adjusting low priority signal competition frequency, optimizing high priority signal access window, adjusting signal frame transmission interval, optimizing bus load balancing, obtaining signal access scheduling parameter;

[0053] S4: based on the signal access scheduling parameter, calling dynamic key management rule, identifying abnormal data frame, adjusting data encryption method, optimizing key matching, updating data integrity check, obtaining secure data integrity parameter;

[0054] S5: based on the secure data integrity parameter, extracting data frame size, sending frequency, arbitration delay, calculating abnormal score, adjusting abnormal data frame priority, screening abnormal data, obtaining vehicle network anomaly detection judgment result.

[0055] Compared with the prior art, the advantages and positive effects of the present application are:

[0056] In the present application, by optimizing the bit rate, data packet length and padding byte ratio of data frame information, the data transmission efficiency is improved, the occupation of bandwidth by invalid data transmission is reduced, the data transmission priority screening mechanism is called, the high priority signal obtains more stable transmission path, the signal delay is reduced, based on the real-time monitoring of data flow state and node transmission frequency, the bandwidth allocation ratio is adjusted, the bus congestion caused by burst flow is relieved, the bus resources are more balanced, combined with the analysis of signal arbitration priority and bus access conflict state, the non-emergency signal priority is adjusted, the risk of blocking key data transmission is reduced, the rationality of signal scheduling is improved, the data encryption and integrity check mechanism is adopted, the safety of data transmission is ensured, the risk of data tampering or information leakage is reduced, the data abnormal score and abnormal detection method are introduced, the abnormal data is screened based on the data frame characteristics, the identification ability of abnormal behavior is improved, and the network security hidden danger is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 The system flowchart of the present application;

[0058] Figure 2Flow chart for obtaining the data transmission optimization module in the application;

[0059] Figure 3 Flow chart for obtaining the bandwidth management module in the application;

[0060] Figure 4 Flow chart for obtaining the signal scheduling control module in the application;

[0061] Figure 5 Flow chart for obtaining the encryption security control module in the application;

[0062] Figure 6 Flow chart for obtaining the exception detection module in the application. DETAILED DESCRIPTION

[0063] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0064] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0065] Please refer to Figure 1 The present application provides a technical solution: a high-efficiency vehicle network control system based on CAN-FD includes

[0066] The data transmission optimization module is based on CAN-FD data frame information, including bit rate, data packet length, number of padding bytes, calling vehicle control signal, state information data, diagnostic data stream data stream type information, filtering transmission priority threshold, calculating data packet length adjustment parameter, judging padding byte proportion, calling arbitration stage bit rate, adjusting data segment rate, and obtaining updated data frame parameter;

[0067] The bandwidth management module is based on the updated data frame parameter, extracts data bus flow state, node data transmission frequency, average load value, calculates bandwidth usage mean value, filters instantaneous flow overrun node, adjusts low priority node bandwidth proportion, and generates dynamic bandwidth allocation result;

[0068] The signal scheduling control module extracts vehicle network signal priority data based on the dynamic bandwidth allocation result, calls vehicle control signal arbitration priority, judges bus access conflict state, adjusts non-emergency signal priority, and generates signal priority adjustment result;

[0069] The encryption security control module extracts encryption key based on the signal priority adjustment result, calls data frame symmetric encryption parameter, identifies encrypted data and integrity check code, calls data receiving end pre-shared key, compares data integrity verification value, and generates data integrity test result.

[0070] The abnormality detection module extracts data frame size, sending frequency, arbitration delay based on the data integrity test result, calculates data anomaly score, calls isolated forest anomaly detection algorithm, filters anomaly score over-limit data, and generates vehicle network anomaly detection judgment result.

[0071] The updated data frame parameters include data packet length adjustment parameter, padding byte proportion, bit rate adjustment value, data segment rate setting, dynamic bandwidth allocation result includes bandwidth usage mean value, instantaneous flow over-limit node identification, low priority node bandwidth proportion setting, signal priority adjustment result includes signal priority data, arbitration priority parameter, bus access conflict state identification, non-emergency signal priority adjustment value, data integrity test result includes encryption key parameter, symmetric encryption parameter, encrypted data identification, integrity check code value, pre-shared key, integrity verification result, vehicle network anomaly detection judgment result includes data frame size parameter, sending frequency value, arbitration delay time, data anomaly score value, anomaly detection algorithm, anomaly score over-limit data identification.

[0072] Please refer to Figure 2 , the data transmission optimization module includes:

[0073] The parameter filtering submodule filters data stream type information based on CAN-FD data frame information, including bit rate, data packet length, number of padding bytes, vehicle control signal, state information data, and diagnostic data stream data, analyzes transmission priority and sets threshold, filters data stream, and obtains filtered data frame parameters.

[0074] First, the real-time control signals and state information data of the vehicle are collected through the data acquisition system, and then the data is preliminarily processed, such as extracting bit rate, data packet length, number of padding bytes, and other basic parameters. Based on the length of the data packet and the padding byte, the transmission priority threshold is set, the data stream is filtered, and those data streams that do not meet the priority requirements are excluded to ensure that critical data can be transmitted first. For example, during emergency braking, related control signals and state information must be processed and transmitted first to improve response speed and overall system safety, and filtered data frame parameters are obtained.

[0075] The data packet length adjustment submodule calls the screened data frame parameters, identifies the proportion of padding bytes, judges whether it exceeds the proportion, and uses the formula:

[0076]

[0077] The optimized data packet length is calculated;

[0078] L opt represents the optimized data packet length, L ori represents the original data packet length, P k represents the number of padding bytes of the kth data frame, N eff represents the number of valid data frames, R bit represents the data segment bit rate, R arb represents the arbitration phase bit rate, and M represents the total number of data frames.

[0079] In the data packet length adjustment submodule, suppose a transportation company needs to optimize the transmission efficiency of CANFD data frames in its fleet management system to reduce the proportion of unnecessary padding bytes and improve the utilization rate of effective payload. The screened data frame parameters are called, the original data packet length L ori , the padding byte set P k and the number of valid data frames N eff of the current data packet are extracted, and the data segment bit rate R bit and the arbitration phase bit rate R arb are obtained from historical transmission data. Suppose the current original data packet length L ori = 150 bytes, the padding bytes are P1 = 12, P2 = 10, P3 = 14, P4 = 13, P5 = 11, P6 = 10, P7

[0080] = 15, P8 = 9, P9 = 12, P 10 = 13 bytes, and the total sum of padding bytes is calculated as:

[0081]

[0082] The number of valid data frames N eff = 10, and the proportion of padding bytes is calculated as:

[0083] The threshold value of the proportion of padding bytes is set to 12 bytes, and it is judged whether it exceeds the reasonable range. Here, the average of the padding bytes is 11.9 bytes, which is lower than the threshold value, so the data packet length can be adjusted appropriately to optimize data transmission. Suppose the data segment bit rate R bit = 500kbps, the arbitration phase bit rate R arb = 100kbps, and the absolute value of the bit rate difference is calculated:

[0084] |R bit -R arb |=|500-100|=400;

[0085] Calculate the optimized data packet length L opt :

[0086]

[0087] The final optimized data packet length is 388 bytes, which makes the data packet length more suitable for actual data requirements, improves data frame utilization and reduces invalid padding.

[0088] The data segment rate optimization submodule calls the optimized data packet length, identifies the bit rate ratio relationship, adjusts the data segment rate, and obtains the updated data frame parameters;

[0089] Calculate the ratio between the data segment bit rate and the arbitration stage bit rate, and adjust the data segment rate according to the ratio result to ensure that important traffic signal data can be transmitted quickly and accurately during peak traffic periods, for example, if the original data segment rate is 200kbps during peak traffic, and after adjustment, the data segment rate is adjusted to 250kbps according to real-time traffic flow data, thereby improving the speed and accuracy of signal processing, ensuring timely updating of traffic signals, and obtaining the updated data frame parameters of key traffic signals after optimization.

[0090] Please refer to Figure 3 , the bandwidth management module includes:

[0091] The bus flow monitoring submodule extracts the data bus flow state based on the updated data frame parameters, monitors the node data transmission frequency, calculates the data bus average load value, analyzes the bandwidth usage, and obtains the data bus flow distribution;

[0092] Data collected from multiple nodes of the vehicle network is used to monitor the data flow and communication frequency between nodes of the entire system, which is the key to ensuring data mobility and optimizing network load. Through real-time monitoring, it can be analyzed which nodes become network bottlenecks due to frequent data transmission, for example, during peak periods, the vehicle's emergency braking system frequently sends signals, and data points are used to calculate the average load value of the entire network. Through regular analysis of data, not only can the load trend of the network be predicted, but also the system administrator can identify performance bottlenecks, thereby optimizing data flow and improving network efficiency. The monitoring and analysis process ultimately obtains the data bus flow distribution, providing a decision support basis for the next step of bandwidth management.

[0093] The node bandwidth allocation submodule calls the data bus traffic distribution, filters the instantaneous traffic over-limit nodes, and uses the formula:

[0094]

[0095] The bandwidth adjustment amount of the low-priority node is calculated, and the node bandwidth adjustment parameter is obtained;

[0096] Wherein, B adj is the bandwidth adjustment amount of the low-priority node, B avg is the mean value of the node bandwidth usage,

[0097] F node is the current node data transmission frequency, F thr is the traffic threshold, M node is the total number of node connections,

[0098] U curr is the current data bus load;

[0099] First, all nodes are filtered to find those nodes whose instantaneous traffic is over-limit. The judgment standard of instantaneous traffic over-limit is based on the set traffic threshold F thr Comparison, if the transmission frequency F node of a node exceeds the threshold, the node is marked as an over-limit node. For example, in an intelligent transportation system, the average data transmission frequency of a traffic monitoring camera is set to 500 Mbps, and the current detected data traffic reaches 650 Mbps, so the camera node is considered to be in over-limit state.

[0100] The mean value of the bandwidth usage B avg of all nodes is calculated, which is the statistical mean value of the bandwidth occupation of all nodes. Assuming that there are 5 nodes in the system, and their bandwidth occupation is 400 Mbps, 450 Mbps, 470 Mbps, 500 Mbps and 530 Mbps, the mean value of the bandwidth is calculated as:

[0101]

[0102] Based on the information of the traffic over-limit node, the bandwidth proportion of the node is adjusted, and the formula is:

[0103]

[0104] Wherein, the real-time load U curr of the current system bus is set to 80%, i.e. 0.8, and the total number of connections M node of the node is set to 10, and the numerical calculation is carried out:

[0105]

[0106] The bandwidth adjustment value of the node is 482 Mbps, which indicates the recommended bandwidth allocation of the node under the current load condition to avoid over-limit impact on other nodes. The system aggregates the adjustment bandwidth parameters of all nodes to obtain the node bandwidth adjustment parameter, which provides a basis for the next step of dynamic bandwidth allocation.

[0107] The dynamic bandwidth adjustment submodule calls the node bandwidth adjustment parameter, allocates the bandwidth proportion of low-priority nodes according to the node bandwidth adjustment condition, adjusts the data flow priority, and generates a dynamic bandwidth allocation result.

[0108] According to the current network demand and the actual performance of each node, the bandwidth is allocated, which involves a complex decision-making mechanism. The data flow direction needs to be dynamically adjusted according to the real-time usage of home devices to optimize network performance and reduce latency. The system compares real-time data with preset performance indicators and adjusts the bandwidth proportion of low-priority nodes as needed to ensure that critical tasks such as security monitoring data transmission are not affected. The system can flexibly respond to changes in various network conditions to optimize the performance of the entire network, and finally obtain a dynamic bandwidth allocation result.

[0109] Please refer to Figure 4 , the signal scheduling control module includes:

[0110] The signal priority extraction submodule extracts vehicle network signal priority data based on the dynamic bandwidth allocation result, calculates the initial priority value, signal type, bandwidth occupation ratio, and data transmission rate, and obtains the signal priority interval characteristics.

[0111] The extraction process of vehicle network signal priority data involves obtaining signal types and their related bandwidth occupation ratios and data transmission rates from the database of the vehicle control system. The data is dynamically updated in real time. For example, in a practical embodiment, during highway driving, the dynamic bandwidth allocation system adjusts the priority data of the signals according to the speed and traffic density of the vehicle. The vehicle control system adjusts the transmission priority of the brake light or turn signal based on this data to ensure that critical signals are transmitted preferentially under congested network conditions. The initial priority value of the signal is dynamically calculated by the base station based on the position and speed of the vehicle. For example, when the vehicle approaches an intersection, the priority of signals related to the intersection (such as turn signals) is increased. The priority value is calculated by a specific value, such as a turn signal with a priority value of 5 adjusted to 8. At the same time, the bandwidth utilization rate and conflict coefficient of each priority interval are calculated, which helps the network controller optimize the overall signal processing performance. For example, the bandwidth utilization rate calculation formula is the ratio of the used bandwidth to the total bandwidth, and the signal priority interval characteristics are obtained, such as the bandwidth utilization rate of the priority 5-8 interval is 70%, and the conflict coefficient is 0.2. The calculation result supports further network signal scheduling decisions.

[0112] The signal arbitration judgment submodule calls the signal priority interval feature, judges the bus access conflict state, calculates the signal scheduling conflict coefficient and the bus load balancing coefficient, and obtains the non-emergency signal conflict influence value;

[0113] According to the current bandwidth condition, the bus access state of each priority signal is calculated, for example, in a city congestion environment, the vehicle control system optimizes signal transmission using feature data to avoid information loss, the specific process includes judging the access conflict state of the bus, for example, by comparing the calculated bus load balancing coefficient with the preset threshold value, if the current load coefficient exceeds the threshold value, the system automatically reduces the priority of non-emergency signals, this judgment is based on dynamic data, for example, the calculation formula of the bus load balancing coefficient is the ratio of the number of active signals to the total number of signals, for example, if there are 7 active signals in 10 signals, the load coefficient is 0.7, according to the coefficient, the control system adjusts the signal priority, increases the priority of critical signals such as brake lights, and reduces the priority of non-emergency signals such as window control, calculates the priority conflict influence factor of the non-emergency signal group, and obtains the non-emergency signal conflict influence value, for example, the calculation of the conflict influence factor considers the signal type and priority, for example, the conflict influence factor of the non-emergency signal is 0.3.

[0114] The signal priority adjustment submodule reassigns the priority based on the non-emergency signal conflict influence value, using the formula:

[0115]

[0116] Adjust the priority of non-emergency signals to generate a signal priority adjustment result;

[0117] Where S adj represents the signal priority adjustment value, S ori represents the initial priority value, C conf represents the priority conflict influence factor, B util represents the bandwidth utilization rate, W bal represents the bus load balancing coefficient, V dyn represents the dynamic bandwidth allocation result, V thr represents the priority adjustment threshold value;

[0118] The priority reassignment coefficient of non-emergency signals is calculated, which involves signal type, current network condition and bus load condition, for example, in a highway environment, the vehicle control system needs to ensure that the priority of emergency signals (such as brake lights) is the highest, and the priority of in-vehicle entertainment system signals is reduced to prevent network congestion from affecting the transmission of safety signals, now let's calculate with specific values, set:

[0119] The initial priority S ori = 3 (for example, the control signal of a certain sound system);

[0120] Priority conflict influence factor C conf = 0.1 (calculated by comparing the priority of non-urgent signals with that of urgent signals);

[0121] Bandwidth utilization rate B util = 0.75 (calculated by the ratio of used bandwidth to total bandwidth, assuming that the total bandwidth is 10 Mbps and the used bandwidth is 7.5 Mbps);

[0122] Bus load balancing coefficient W bal = 0.5 (calculated by the ratio of the number of currently active signals on the bus to the maximum number of signals that can be carried, assuming that the number of currently active signals is 5 and the maximum number of signals that can be carried is 10, then W bal = 5 / 10 = 0.5);

[0123] Dynamic bandwidth allocation result V dyn = 10 (this value is obtained by monitoring the vehicle communication module and reflects the current total bandwidth allocation);

[0124] Priority adjustment threshold V thr = 5 (this threshold is set according to historical data and represents the maximum priority adjustment allowed by the current network load);

[0125] Substitute the above values into the formula to calculate:

[0126]

[0127] Calculation result S adj = 1.376, which means that the priority of the non-urgent signal is reduced, further reducing network congestion, improving the priority of critical signals (such as brake signals) in emergency situations, preventing safety signals from being delayed due to bus conflicts, and thus ensuring the safety response capability of vehicles in complex traffic environments.

[0128] Please refer to Figure 5 The encryption security control module includes:

[0129] The key extraction sub-module extracts the encryption key based on the signal priority adjustment result, analyzes the data frame, filters the matching symmetric encryption parameters, identifies the key validity, and generates a valid encryption key;

[0130] In practical application scenarios, for example, when vehicles exchange information through a vehicle networking system, the security of the data must first be confirmed, a suitable key must be selected from a set of preconfigured key libraries to encrypt the data, the validity period of each key and its compatibility with the current communication protocol must be analyzed, and then the most suitable key must be selected, for example, if a key is about to expire or does not support the current encryption algorithm, the system will exclude this key, the system can ensure that the selected key can provide the best security and compatibility, the refinement process includes checking the validity period of each key, comparing its matching degree with the signal priority, and evaluating its encryption efficiency in the current vehicle networking environment, if vehicle A needs to send encrypted data to vehicle B, the system will select a key with the highest encryption level and within the validity period from the key library, to ensure that the information is not intercepted or tampered with by a third party during transmission, and finally generate a valid encryption key.

[0131] The data encryption identification sub-module calls the valid encryption key, parses the data frame structure, identifies the encrypted data and the integrity check code, and uses the formula:

[0132]

[0133] The data encryption consistency value is calculated, the integrity check code is compared, and the encrypted data analysis result is obtained;

[0134] wherein, C E represents the data encryption consistency value, D enc represents the encrypted data, D key represents the key encrypted data, K j represents the weight of the encryption parameter group, S j represents the data packet security parameter, and m represents the number of encryption parameter groups.

[0135] The valid encryption key is called to parse the data frame and identify the encrypted data and the integrity check code, which is crucial in high-security data transmission scenarios, for example, in the communication process between autonomous vehicles, vehicle A must ensure the integrity and security of the data when sending sensor data to vehicle B, the data packet structure is parsed to determine the components of the data packet, including the payload, the encrypted header information, and the integrity check code, the key encrypted data part is extracted and compared with the original encrypted data, and the data encryption consistency value is calculated to measure the consistency of data encryption and decryption;

[0136] Suppose the encrypted data D enc in a data packet is 2048, and the key encrypted data D key is 2025, and the data packet security parameter S jS1, S2, S3 are the encryption parameters, and the value range of S1, S2, S3 is between 0.8 to 1.2, taking S1 = 1.1, S2 = 0.9, S3 = 1.0, and the weight K of the encryption parameter group j Taking 0.5, 0.3 and 0.2, the calculation process of the encryption consistency value is as follows:

[0137]

[0138] The denominator part is calculated as follows:

[0139]

[0140] The numerator part is calculated as follows: |2048-2025| = 23;

[0141] The final calculation is as follows:

[0142] The encryption consistency value C E The calculation result is 22.77, which can be compared with the set integrity check threshold value, for example, if the integrity check threshold value is set to 25, then 22.77 is less than 25, indicating that the data encryption and decryption consistency is high, and the data has not been tampered with, if the calculation result exceeds 25, it indicates that the data has been interfered or tampered with in the transmission process, and needs to be re-encrypted or retransmitted, in vehicle communication, the calculation can help vehicle B to judge whether the data packet from vehicle A is trustworthy, and finally obtain the encrypted data analysis result.

[0143] The integrity check sub-module calls the encrypted data analysis result, analyzes the pre-shared key, calculates the integrity check value, filters the data integrity deviation range, and obtains the data integrity test result;

[0144] It is crucial in any scenario involving data transmission, for example, in financial transactions, ensuring the integrity of transaction data can prevent financial fraud, first call the encrypted data analysis result, then analyze the pre-shared key of the data receiving end, and finally check the integrity of the data, which includes calculating the integrity check value of the data using the pre-shared key, and comparing it with the check code in the data packet, if the check value matches, it means that the data has not been tampered with in the transmission process, such as when conducting high-value financial transactions, the system will check the integrity check code of each transaction to confirm the authenticity and integrity of the data, through filtering the data integrity deviation range, to ensure the safety of the transaction, and finally generate the data integrity test result.

[0145] Please refer to Figure 6 , the anomaly detection module includes:

[0146] The data error detection and calibration submodule extracts data frame size, transmission frequency, arbitration delay based on the data integrity test result, calculates the integrity deviation value, compares the integrity reference value, and obtains the data integrity deviation analysis result;

[0147] The comparison and analysis of transmission frequency and data frame size, for example, in a vehicle network, assuming that 100 frames of data should be sent per second, and each frame of data is 1 KB in size, if the actual transmission frequency drops to 80 frames per second, or the data frame size changes to 1.2 KB, it can be preliminarily judged that the data integrity is damaged, which is realized by monitoring the network transmission efficiency and frame loss rate, the specific steps of calculating the data integrity deviation value include comparing the actual number of data frames transmitted per second with the preset reference value to determine whether there is a deviation and the specific value of the deviation, for example, if the detected data frame size continuously exceeds the predetermined threshold, it is considered that data tampering or error has occurred, by comparing the data with the set integrity reference value, for example, comparing the actual transmission frequency with the preset frequency, confirming whether the deviation exceeds the allowed range, the steps together complete the evaluation of data integrity, determine whether there is a case where the integrity deviation exceeds the threshold, and obtain the data integrity deviation analysis result.

[0148] The data anomaly score calculation submodule calls the data integrity deviation analysis result, calculates each data anomaly score value, and combines the integrity deviation situation, using the formula:

[0149]

[0150] Obtain the data anomaly score set;

[0151] Wherein, D s represents the data anomaly score value, F k represents the size of the kth data frame, F th represents the reference value of the data frame size, L k represents the integrity deviation value of the kth data frame, T k represents the transmission frequency of the kth data frame, A k represents the arbitration delay of the kth data frame, A th represents the reference value of the arbitration delay, and M represents the total number of data frames.

[0152] In a vehicle CAN network, assuming that the data integrity deviation analysis result is as follows: the data frame size F k is in the range [0.8 KB, 1.3 KB], the reference value F th is 1.0 KB, the transmission frequency T k is between 75 frames per second and 105 frames per second, the reference transmission frequency T th is 100 frames per second, and the arbitration delay A kBetween 2ms and 12ms, the benchmark arbitration delay A th Set to 5ms

[0153] Assuming that in a monitoring period M = 3, the three groups of data obtained by sampling are as follows:

[0154] F1 = 1.2KB, T1 = 80, A1 = 10ms

[0155] F2 = 0.9KB, T2 = 95, A2 = 4ms

[0156] F3 = 1.3KB, T3 = 70, A3 = 8ms

[0157] Calculate the integrity deviation value L of each data frame k :

[0158] L k = |F k -F th |, that is:

[0159] L1 = |1.2-1.0| = 0.2KB

[0160] L2 = |0.9-1.0| = 0.1KB

[0161] L3 = |1.3-1.0| = 0.3KB

[0162] Calculate the score of each data frame:

[0163]

[0164] Final data anomaly score calculation result:

[0165] D s = D1 + D2 + D3 = 0.00049 + 0.000104 + 0.00125 = 0.001844

[0166] Obtain each data anomaly score value by operation, combined with the integrity deviation, for example, for a specific data packet, if the deviation of its data frame size, sending frequency and arbitration delay from the benchmark value exceeds the set threshold, the anomaly score of the data packet will increase significantly, and a data anomaly score set is obtained.

[0167] The abnormal data screening submodule calls the data anomaly score set, screens the abnormal score data exceeding the limit, marks the abnormal data items, extracts the abnormal features, and generates a vehicle network anomaly detection judgment result

[0168] Screening data items with abnormal scores exceeding the threshold, for example, after calculating the data abnormal score, all data packets exceeding the set threshold (such as a score exceeding 50 points) are marked as abnormal, and the data items include data packets with abnormally low sending frequency, abnormally large data frame size, or abnormally long arbitration delay. The data with abnormal scores exceeding the set threshold is marked, and the abnormal data features are extracted, including analyzing the specific parameters of the data packets marked as abnormal, such as data frame size and sending time interval, to determine the abnormal mode. This involves comparing the abnormal data with normal operation data to verify the accuracy of the abnormal mode, generating a vehicle network anomaly detection judgment result, which will directly affect the network maintenance decision and fault diagnosis.

[0169] The efficient vehicle network control method based on CAN-FD is executed based on the above-mentioned efficient vehicle network control system based on CAN-FD, including the following steps:

[0170] S1: Based on the CAN-FD data frame information, the bit rate, data packet length, and number of padding bytes are extracted, the data packet length adjustment parameter is calculated, the data segment rate is adjusted, the data frame transmission is optimized, and the optimized data frame parameters are obtained;

[0171] S2: Based on the optimized data frame parameters, the data bus flow state, node transmission frequency, and average load value are extracted, the low-priority node bandwidth is adjusted, the high-priority node transmission is optimized, and the dynamic bandwidth allocation result is obtained;

[0172] S3: Based on the dynamic bandwidth allocation result, the signal arbitration priority is called, the signal access conflict is analyzed, the low-priority signal competition frequency is adjusted, the high-priority signal access window is optimized, the signal frame transmission interval is adjusted, and the bus load balancing is optimized, and the signal access scheduling parameter is obtained;

[0173] S4: Based on the signal access scheduling parameter, the dynamic key management rule is called, the abnormal data frame is identified, the data encryption method is adjusted, the key matching is optimized, the data integrity check is updated, and the secure data integrity parameter is obtained;

[0174] S5: Based on the secure data integrity parameter, the data frame size, sending frequency, and arbitration delay are extracted, the abnormal score is calculated, the abnormal data frame priority is adjusted, the abnormal data is screened, and the vehicle network anomaly detection judgment result is obtained.

[0175] The above is only a preferred embodiment of the present application, and does not limit the form of the present application, any skilled person in the art can use the disclosed technical content to make changes or modifications to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application still belongs to the protection scope of the technical solution of the present application.

Claims

1. A high efficient vehicle network control system based on CAN-FD, characterized in that, The system comprises: The data transmission optimization module calls vehicle control information data based on CAN-FD data frame information, filters transmission priority threshold, calculates data packet length adjustment parameter, judges padding byte proportion, adjusts data segment rate, and obtains updated data frame parameter; The bandwidth management module extracts data bus flow state, node data transmission frequency, calculates bandwidth usage mean value, adjusts low-priority node bandwidth proportion based on the updated data frame parameter, and generates dynamic bandwidth allocation result; The bandwidth management module comprises: The bus flow monitoring submodule extracts data bus flow state, monitors node data transmission frequency, calculates data bus average load value, analyzes bandwidth usage based on the updated data frame parameter, and obtains data bus flow distribution; The node bandwidth allocation submodule calls the data bus flow distribution, filters instantaneous flow overrun nodes, and uses the formula: ； to calculate the bandwidth adjustment amount of low-priority nodes, and obtain node bandwidth adjustment parameter; wherein, a bandwidth adjustment amount for low priority nodes, a node bandwidth usage average, a current node data transmission frequency, a traffic threshold, a total number of connections for a node, a current data bus load; The dynamic bandwidth adjustment submodule calls the node bandwidth adjustment parameter, allocates low-priority node bandwidth proportion according to node bandwidth adjustment condition, adjusts data flow priority, and generates dynamic bandwidth allocation result; The signal scheduling control module extracts vehicle network signal priority data based on the dynamic bandwidth allocation result, judges bus access conflict state, adjusts non-emergency signal priority, and generates signal priority adjustment result; The signal scheduling control module comprises: The signal priority extraction submodule extracts vehicle network signal priority data based on the dynamic bandwidth allocation result, counts initial priority value, signal type, bandwidth occupation ratio and data transmission rate, and obtains signal priority interval feature; The signal arbitration judgment submodule calls the signal priority interval feature, judges the bus access conflict state, calculates the signal scheduling conflict coefficient and the bus load balancing coefficient, and obtains the non-emergency signal conflict influence value; The signal priority adjustment submodule re-allocates priority based on the non-emergency signal conflict influence value, uses the formula: ； to adjust the priority of non-emergency signals, and generate signal priority adjustment result; wherein, a representative signal priority adjustment value, a representative initial priority value, a representative priority conflict impact factor, a representative bandwidth utilization, a representative bus load balancing coefficient, a representative dynamic bandwidth allocation result, a representative priority adjustment threshold value; The encryption security control module extracts encryption key based on the signal priority adjustment result, identifies encrypted data and integrity check code, compares data integrity verification value, and generates data integrity verification result; The anomaly detection module extracts data frame size, sending frequency, calculates data anomaly score, filters anomaly score overrun data based on the data integrity verification result, and generates vehicle network anomaly detection judgment result.

2. The high efficient vehicle network control system based on CAN-FD according to claim 1, characterized in that, The updated data frame parameters include data packet length adjustment parameters, fill byte proportion, bit rate adjustment value, data segment rate setting, the dynamic bandwidth allocation result includes bandwidth usage average, instantaneous flow overrun node identification, low priority node bandwidth proportion setting, the signal priority adjustment result includes signal priority data, arbitration priority parameter, bus access conflict state identification, non-emergency signal priority adjustment value, the data integrity verification result includes encryption key parameter, symmetric encryption parameter, encrypted data identification, integrity check code value, pre-shared key, integrity verification result, the vehicle network anomaly detection judgment result includes data frame size parameter, sending frequency value, arbitration delay time, data anomaly score value, anomaly detection algorithm, anomaly score overrun data identification.

3. The CAN-FD based high efficient vehicle network control system according to claim 1, characterized in that, The data transmission optimization module includes: The parameter screening submodule screens data stream type information based on CAN-FD data frame information, including bit rate, data packet length, number of fill bytes, vehicle control signal, state information data, and diagnostic data stream data, analyzes transmission priority and sets a threshold, screens data streams, and obtains screened data frame parameters; The data packet length adjustment submodule calls the screened data frame parameters, identifies the proportion of fill bytes, judges whether it exceeds the proportion, uses the formula: to calculate the optimized data packet length; ； The data segment rate optimization submodule calls the optimized data packet length, identifies the bit rate proportion relationship, adjusts the data segment rate, and obtains the updated data frame parameters. wherein, represents the optimized data packet length, represents the original data packet length, represents the number of padding bytes of the th data frame, represents the number of valid data frames, represents the data segment bit rate, represents the arbitration phase bit rate, represents the total number of data frames; The encryption security control module includes:

4. The high efficient vehicle network control system based on CAN-FD according to claim 1, characterized in that, The key extraction submodule extracts an encryption key based on the signal priority adjustment result, parses the data frame, screens the matching symmetric encryption parameter, identifies the key effectiveness, and generates an effective encryption key; The data encryption identification submodule calls the effective encryption key, parses the data frame structure, identifies the encrypted data and integrity check code, uses the formula: to calculate the data encryption consistency value, compares the integrity check code, and obtains the encrypted data analysis result; The integrity check submodule calls the encrypted data analysis result, parses the pre-shared key, calculates the integrity check value, screens the data integrity deviation range, and obtains the data integrity verification result. ； The anomaly detection module includes: wherein, represents a data encryption consistency value, represents encrypted data, represents key encrypted data, represents a weight of the encryption parameter group, represents a data packet security parameter, represents a number of encryption parameter groups; The data error detection and calibration submodule extracts the data frame size, sending frequency, and arbitration delay based on the data integrity verification result, calculates the integrity deviation value, compares the integrity reference value, and obtains the data integrity deviation analysis result; 5. The high efficient vehicle network control system based on CAN-FD according to claim 4, characterized in that, The data anomaly score calculation submodule calls the data integrity deviation analysis result, calculates each data anomaly score value, combines the integrity deviation situation, uses the formula: to obtain the data anomaly score set; The anomaly data screening submodule calls the data anomaly score set, screens the overrun anomaly score data, marks the abnormal data item, extracts the abnormal feature, and generates the vehicle network anomaly detection judgment result. The CAN-FD-based efficient vehicle network control system according to any one of claims 1-5 is executed, including the following steps: ； ​ in, The score represents the data anomaly rating. Representing the The size of each data frame A baseline value representing the size of a data frame. Representing the The integrity deviation value of each data frame. Representing the The transmission frequency of each data frame Representing the Arbitration delay for each data frame The benchmark value representing the delay in arbitration. Represents the total number of data frames; ​ 6. A high-efficient vehicle network control method based on CAN-FD, characterized in that, ​ S1: Based on CAN-FD data frame information, extract bit rate, data packet length, number of padding bytes, calculate data packet length adjustment parameter, adjust data segment rate, optimize data frame transmission, get optimized data frame parameter; S2: Based on the optimized data frame parameter, extract data bus flow state, node transmission frequency, average load value, adjust low priority node bandwidth, optimize high priority node transmission, get dynamic bandwidth allocation result; S3: Based on the dynamic bandwidth allocation result, call signal arbitration priority, analyze signal access conflict, adjust low priority signal competition frequency, optimize high priority signal access window, adjust signal frame transmission interval, optimize bus load balancing, get signal access scheduling parameter; S4: Based on the signal access scheduling parameter, call dynamic key management rule, identify abnormal data frame, adjust data encryption method, optimize key matching, update data integrity check, get secure data integrity parameter; S5: Based on the secure data integrity parameter, extract data frame size, sending frequency, arbitration delay, calculate abnormal score, adjust abnormal data frame priority, filter abnormal data, get vehicle network anomaly detection judgment result.

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