Efficient vehicle network control system and method based on CAN-FD

Abstract

The invention relates to the technical field of vehicle network control, in particular to an efficient vehicle network control system and method based on CAN-FD, and the system comprises a data transmission optimization module, a bandwidth management module, a signal scheduling control module, an encryption security control module and an anomaly detection module. In the invention, the bit rate of a data frame, the length of a data packet and the proportion of filling bytes are optimized, the transmission efficiency is improved, the bandwidth occupied by invalid data is reduced, a priority screening mechanism is called, the stable transmission of high-priority signals is ensured, the bandwidth allocation is adjusted based on the flow state and node frequency monitoring, the burst flow congestion is relieved, and the resource allocation is balanced; in combination with arbitration priority and bus conflict state, non-emergency signal priority is optimized, key data blocking risk is reduced, encryption and integrity verification are adopted, data security is ensured, tampering and leakage are prevented, data abnormity score and detection are introduced, abnormal data are screened, identification capability is improved, and network potential safety hazard is reduced.

Description

Technical Field

[0001] The present invention 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 Art

[0002] The field of vehicle network control technology includes data communication systems inside and outside the vehicle, which are mainly used to ensure information exchange between various electronic control units ECU of the vehicle to support the 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 protection 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 increase in the complexity of automotive electronic systems.

[0003] Among them, the efficient vehicle network control system based on CAN-FD refers to the use of the CAN-FD protocol to achieve more efficient data communication to meet the vehicle's needs for large bandwidth, low latency and high real-time performance. The patent subject covers the optimization of the CAN-FD data frame structure to increase the data transmission rate, and adopts an adaptive arbitration mechanism to reduce bus conflicts. For high data traffic scenarios, a dynamic load balancing strategy is used to allocate data transmission priority to alleviate network congestion problems. To ensure data integrity and security, the patent designs a message integrity verification method based on a hash function, and combines encryption and authentication technology to improve the network's anti-interference ability. The system optimizes the clock synchronization accuracy between nodes through a time synchronization mechanism to improve the certainty of data transmission.

[0004] In the process of data transmission, the existing technology does not fully optimize the data frame structure, resulting in a high proportion of padding bytes, increasing the bus load and reducing bandwidth utilization. The data transmission priority mechanism fails to dynamically adapt to the network load, resulting in delays in high-priority signals in high-traffic environments, affecting the real-time performance of vehicle control. The bandwidth allocation method mainly relies on fixed rules and fails to make dynamic adjustments based on instantaneous traffic changes. It is easy to cause bus congestion under sudden loads, reducing the communication efficiency of some nodes. Signal priority management does not fully consider bus access conflicts, resulting in non-critical signals occupying bandwidth resources and affecting the transmission stability of critical control signals. 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 mostly uses fixed threshold judgments and lacks the ability to adapt to complex abnormal behaviors, resulting in hidden attacks or sudden abnormalities not being identified in a timely manner. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an efficient vehicle network control system and method based on CAN-FD.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A high-efficiency vehicle network control system based on CAN-FD includes:

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

[0008] The bandwidth management module extracts the data bus traffic status and the node data transmission frequency based on the updated data frame parameters, calculates the bandwidth usage average, adjusts the bandwidth proportion of low priority nodes, and generates a dynamic bandwidth allocation result;

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

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

[0011] Based on the data integrity check result, the anomaly detection module extracts the data frame size, the transmission frequency, calculates the data anomaly score, filters the data with anomaly score exceeding the limit, and generates the vehicle network anomaly detection judgment result.

[0012] As a further solution of the present invention, the updated data frame parameters include data packet length adjustment parameters, padding byte ratio, bit rate adjustment value, and data segment rate setting; the dynamic bandwidth allocation results include bandwidth usage average, instantaneous traffic exceeding node identifier, and low priority node bandwidth ratio setting; the signal priority adjustment results include signal priority data, arbitration priority parameters, bus access conflict status identifier, and non-emergency signal priority adjustment value; the data integrity check results include encryption key parameters, symmetric encryption parameters, encrypted data identifier, integrity check code value, pre-shared key, and integrity verification results; the vehicle network anomaly detection and determination results include data frame size parameters, sending frequency value, arbitration delay time, data anomaly score value, anomaly detection algorithm, and anomaly score exceeding data identifier.

[0013] As a further solution of the present invention, the data transmission optimization module includes:

[0014] The parameter screening submodule is based on the CAN-FD data frame information, including bit rate, data packet length, number of padding bytes, vehicle control signal, status information data, and diagnostic data stream data. It screens the data stream type information, analyzes the transmission priority and sets the threshold, screens the data stream, and obtains the screened 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 proportion, using the formula:

[0016]

[0017] Calculate the optimized data packet length;

[0018] Among them, 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 bit rate in 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 updated data frame parameters.

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

[0021] 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 based on the updated data frame parameters;

[0022] The node bandwidth allocation submodule calls the data bus traffic distribution, filters out nodes with instantaneous traffic exceeding the limit, and uses the formula:

[0023]

[0024] Calculate the bandwidth adjustment amount of the low priority node to obtain the node bandwidth adjustment parameter;

[0025] Among them, B adj is the bandwidth adjustment amount of low priority nodes, B avg is the mean value of node bandwidth usage,

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

[0027] The dynamic bandwidth adjustment submodule calls the node bandwidth adjustment parameters, allocates bandwidth proportions of low-priority nodes according to the node bandwidth adjustment conditions, adjusts data flow priorities, and generates dynamic bandwidth allocation results.

[0028] As a further solution of the present invention, the signal scheduling control module includes:

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

[0030] 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 impact value;

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

[0032]

[0033] Adjusting the priority of non-emergency signals and generating signal priority adjustment results;

[0034] Among them, S adj Represents the signal priority adjustment value, S ori Represents the initial priority value, C conf represents the priority conflict impact factor, B util represents bandwidth utilization, W bal Represents the bus load balancing factor, V dyn Represents the dynamic bandwidth allocation result, V thr Indicates the priority adjustment threshold.

[0035] As a further solution of the present invention, the encryption security control module includes:

[0036] The key extraction submodule extracts the encryption key based on the signal priority adjustment result, parses the data frame, screens the matching symmetric encryption parameters, identifies the key validity, and generates a 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] Calculate the data encryption consistency value, compare the integrity check code, and obtain the encrypted data analysis result;

[0040] Among them, C E Represents the data encryption consistency value, D enc Represents encrypted data, D key Represents the key to encrypt the 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;

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

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

[0043] The data error detection and calibration submodule extracts the data frame size, transmission frequency, arbitration delay, calculates the integrity deviation value, compares the integrity reference value, and obtains the data integrity deviation analysis result based on the data integrity check result;

[0044] The data anomaly score calculation submodule calls the data integrity deviation analysis results, calculates the anomaly score value of each data item, and uses the formula based on the integrity deviation situation:

[0045]

[0046] Get a data anomaly score set;

[0047] Among them, D s Represents the data anomaly score, F k represents the size of the kth data frame, F th Represents the benchmark value of the data frame size, L k represents the integrity deviation value of the kth data frame, T k represents the sending frequency of the kth data frame, A k represents the arbitration delay of the kth data frame, A th represents the base value of arbitration delay, M represents the total number of data frames;

[0048] The abnormal data screening submodule calls the data anomaly score set, screens the over-limit abnormal score data, marks the abnormal data items, extracts abnormal features, and generates a vehicle network anomaly detection and determination result.

[0049] The CAN-FD-based efficient vehicle network control method is executed based on the above-mentioned CAN-FD-based efficient vehicle network control system, and comprises the following steps:

[0050] S1: Based on the CAN-FD data frame information, extract the bit rate, data packet length, and number of padding bytes, calculate the data packet length adjustment parameters, adjust the data segment rate, optimize the data frame transmission, and obtain the optimized data frame parameters;

[0051] S2: Based on the optimized data frame parameters, extract the data bus traffic status, node transmission frequency, and average load value, adjust the bandwidth of low-priority nodes, optimize the transmission of high-priority nodes, and obtain a dynamic bandwidth allocation result;

[0052] S3: Based on the dynamic bandwidth allocation result, call the signal arbitration priority, analyze the signal access conflict, adjust the low priority signal competition frequency, optimize the high priority signal access window, adjust the signal frame transmission interval, optimize the bus load balance, and obtain the signal access scheduling parameter;

[0053] S4: Based on the signal access scheduling parameters, calling dynamic key management rules, identifying abnormal data frames, adjusting data encryption methods, optimizing key matching, updating data integrity check, and obtaining security data integrity parameters;

[0054] S5: Based on the security data integrity parameter, extract the data frame size, transmission frequency, arbitration delay, calculate the anomaly score, adjust the priority of the abnormal data frame, filter the abnormal data, and obtain the vehicle network anomaly detection result.

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

[0056] In the present invention, by optimizing the bit rate, data packet length and proportion of padding bytes of data frame information, data transmission efficiency is improved, bandwidth occupation by invalid data transmission is reduced, a data transmission priority screening mechanism is called to enable high-priority signals to obtain a more stable transmission path, signal delay is reduced, and based on real-time monitoring of data flow status and node transmission frequency, the bandwidth allocation ratio is adjusted to alleviate bus congestion caused by burst traffic and make bus resources more balanced. In combination with the analysis of signal arbitration priority and bus access conflict status, the priority of non-emergency signals is adjusted to reduce the risk of key data transmission being blocked and the rationality of signal scheduling is improved. A data encryption and integrity verification mechanism is adopted to ensure the security of data transmission and reduce the risk of data tampering or information leakage. A data anomaly scoring and anomaly detection method are introduced to screen abnormal data based on data frame features, improve the ability to identify abnormal behaviors and reduce network security risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a system flow chart of the present invention;

[0058] Figure 2This is a flowchart of obtaining the data transmission optimization module in the present invention;

[0059] Figure 3 This is a flow chart of obtaining the bandwidth management module in the present invention;

[0060] Figure 4 This is an acquisition flow chart of the signal scheduling control module in the present invention;

[0061] Figure 5 This is a flowchart for obtaining the encryption security control module in the present invention;

[0062] Figure 6 This is a flowchart for obtaining the abnormality detection module in the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0065] See also Figure 1 The present invention provides a technical solution: an efficient vehicle network control system based on CAN-FD comprises:

[0066] The data transmission optimization module is based on the CAN-FD data frame information, including bit rate, data packet length, and number of padding bytes. It calls the data stream type information of the vehicle control signal, status information data, and diagnostic data stream, filters the transmission priority threshold, calculates the data packet length adjustment parameter, determines the proportion of padding bytes, calls the arbitration phase bit rate, adjusts the data segment rate, and obtains the updated data frame parameters.

[0067] Based on the updated data frame parameters, the bandwidth management module extracts the data bus traffic status, node data transmission frequency, and average load value, calculates the bandwidth usage average, filters out nodes with instantaneous traffic exceeding the limit, adjusts the bandwidth share of low-priority nodes, and generates dynamic bandwidth allocation results;

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

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

[0070] Based on the data integrity check results, the anomaly detection module extracts the data frame size, transmission frequency, arbitration delay, calculates the data anomaly score, calls the isolation forest anomaly detection algorithm, filters the data with anomaly score exceeding the limit, and generates the vehicle network anomaly detection judgment result.

[0071] The updated data frame parameters include data packet length adjustment parameters, padding byte ratio, bit rate adjustment value, and data segment rate setting. The dynamic bandwidth allocation results include bandwidth usage average, instantaneous traffic exceeding node identification, and low priority node bandwidth ratio setting. The signal priority adjustment results include signal priority data, arbitration priority parameters, bus access conflict status identification, and non-emergency signal priority adjustment value. The data integrity check results include encryption key parameters, symmetric encryption parameters, encrypted data identification, integrity check code value, pre-shared key, and integrity verification results. The vehicle network anomaly detection and judgment results include data frame size parameters, sending frequency value, arbitration delay time, data anomaly score value, anomaly detection algorithm, and anomaly score exceeding data identification.

[0072] See also Figure 2 , the data transmission optimization module includes:

[0073] The parameter screening submodule is based on the CAN-FD data frame information, including bit rate, data packet length, number of padding bytes, vehicle control signal, status information data, and diagnostic data stream data. It screens the data stream type information, analyzes the transmission priority and sets the threshold, screens the data stream, and obtains the screened data frame parameters.

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

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

[0076]

[0077] Calculate the optimized data packet length;

[0078] Among them, 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 bit rate in the arbitration phase, 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 of the payload. The filtered data frame parameters are called to extract the original data packet length L of the current data packet. ori , fill byte set P k And the number of valid data frames N eff , and obtain the data segment bit rate R from the historical transmission data bit and the arbitration phase bit rate R arb , assuming that the current original data packet length is L ori = 150 bytes, the padding bytes are P 1 =12,P 2 =10,P 3 =14,P 4 =13,P 5 =11,P 6 =10,P 7

[0080] =15,P 8 =9,P 9 =12,P 10 = 13 bytes, the total number of padding bytes is:

[0081]

[0082] Number of valid data frames N eff =10, calculate the percentage of padding bytes:

[0083] The padding byte ratio threshold is set to 12 bytes to determine whether it exceeds the reasonable range. The average padding byte ratio of 11.9 bytes is lower than the threshold. Therefore, the packet length can be adjusted appropriately to optimize data transmission. Assuming that the data segment bit rate R bit =500kbps, arbitration stage bit rate R arb =100kbps, calculate the absolute value of the bit rate difference:

[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. This adjustment scheme makes the data packet length more in line with 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, adjusts the data segment rate, and obtains the updated data frame parameters;

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

[0090] See also Figure 3 , the bandwidth management module includes:

[0091] The bus traffic monitoring submodule extracts the data bus traffic status based on the updated data frame parameters, 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;

[0092] Data collected from multiple nodes in the vehicle network is used to monitor the data flow of the entire system and the communication frequency between nodes. This step is the key to ensuring data fluidity and optimizing network load. Through real-time monitoring, it is possible to analyze which nodes become network bottlenecks due to frequent data transmission. For example, during peak hours, the vehicle's emergency braking system frequently sends signals, and data points are used to calculate the average load value of the entire network. By regularly analyzing the data, it is not only possible to predict the load trend of the network, but also to help system administrators identify performance bottlenecks, thereby optimizing data flow and improving network efficiency. The monitoring and analysis process ultimately obtains the data bus traffic distribution, which provides a decision support basis for the next step of bandwidth management.

[0093] The node bandwidth allocation submodule calls the data bus traffic distribution to filter out nodes with instantaneous traffic exceeding the limit, using the formula:

[0094]

[0095] Calculate the bandwidth adjustment amount of the low priority node to obtain the node bandwidth adjustment parameter;

[0096] Among them, B adj is the bandwidth adjustment amount of low priority nodes, B avg is the mean value of node bandwidth usage,

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

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

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

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

[0101]

[0102] Based on the information of the node with excessive traffic, adjust its bandwidth share using the formula:

[0103]

[0104] Among them, the real-time load U of the current system bus curr Set to 80%, or 0.8, the total number of connections of this node is M node Set it to 10 and substitute the value to calculate:

[0105]

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

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

[0108] Bandwidth is allocated based on current network demand and the actual performance of each node. The process involves a complex decision-making mechanism that requires dynamic adjustment of data flow based on 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 share of low-priority nodes as needed to ensure that critical tasks such as data transmission for security monitoring are not affected. The system can flexibly respond to changes in various network conditions, optimize the performance of the entire network, and ultimately obtain dynamic bandwidth allocation results.

[0109] See also Figure 4 , the signal scheduling control module includes:

[0110] The signal priority extraction submodule extracts the vehicle network signal priority data based on the dynamic bandwidth allocation results, and calculates the initial priority value, signal type, bandwidth occupancy ratio and data transmission rate to obtain the signal priority interval characteristics;

[0111] The process of extracting vehicle network signal priority data involves obtaining the signal type and its related bandwidth occupancy ratio and data transmission rate from the database of the vehicle control system. The data is updated in real time and dynamically. For example, a practical implementation is that when driving on a highway, the dynamic bandwidth allocation system adjusts the signal priority data according to the vehicle speed and traffic density. The vehicle control system adjusts the transmission priority of the brake light or turn signal based on this data to ensure that key signals are transmitted first under congested network conditions. The initial priority value of the signal is dynamically calculated by the base station based on the vehicle's position and speed. For example, when the vehicle approaches an intersection, the priority of the signal related to the intersection (such as the turn signal) is increased. Through specific numerical calculations, if the signal type is a turn signal, its priority value is adjusted from 5 to 8. At the same time, the bandwidth utilization and conflict coefficient of each priority interval are calculated. The calculation helps the network controller optimize the overall signal processing performance. For example, the bandwidth utilization calculation formula is the ratio of the used bandwidth to the total bandwidth, and the signal priority interval characteristics are obtained. For example, the bandwidth utilization of the priority 5-8 interval is 70%, and the conflict coefficient is 0.2. The calculation results support further network signal scheduling decisions.

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

[0113] The bus access status of each priority signal is calculated according to the current bandwidth conditions. For example, in a congested urban environment, the vehicle control system uses characteristic data to optimize signal transmission and avoid information loss. The specific process includes judging the access conflict status of the bus, such as by comparing the calculated bus load balancing coefficient with the preset threshold. If the current load coefficient exceeds the threshold, 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 current number of active signals to the total number of signals. For example, 7 out of 10 signals are active, and the load coefficient is 0.7. According to the coefficient, the control system adjusts the signal priority, increases the priority of key signals such as brake lights, and reduces the priority of non-emergency signals such as window control. The priority conflict impact factor of the non-emergency signal group is calculated, and the non-emergency signal conflict impact value is obtained. For example, the calculation of the conflict impact factor takes into account the signal type and priority. For example, the conflict impact factor of the non-emergency signal is 0.3.

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

[0115]

[0116] Adjusting the priority of non-emergency signals and generating signal priority adjustment results;

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

[0118] The priority redistribution coefficient of non-emergency signals is calculated. The calculation involves the signal type, current network status and bus load. For example, in a highway environment, the vehicle control system needs to ensure that emergency signals (such as brake lights) have the highest priority, while reducing the priority of the in-vehicle entertainment system signal to prevent network congestion from affecting the transmission of safety signals. Now bring in specific numerical calculations, assuming:

[0119] Initial priority S ori =3 (e.g. control signal of a sound system);

[0120] Priority conflict impact factor C conf =0.1 (calculated by comparing the priorities of non-emergency signals and emergency signals);

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

[0122] Bus load balancing factor W bal =0.5 (the calculation method is the ratio of the current number of active signals on the bus to the maximum number of signals that can be carried. Assuming that the current number of active signals is 5 and the maximum number of carried signals is 10, then W bal =5 / 10=0.5);

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

[0124] Priority adjustment threshold V thr =5 (this threshold is set based on historical data and represents the maximum priority adjustment range 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-emergency signal is lowered, further reducing network congestion, increasing the priority of key signals (such as brake signals) in emergency situations, and preventing safety signal delays due to bus conflicts, thereby ensuring the vehicle's safety response capabilities in complex traffic environments.

[0128] See also Figure 5 , the encryption security control module includes:

[0129] The key extraction submodule extracts the encryption key based on the signal priority adjustment result, parses the data frame, screens the matching symmetric encryption parameters, identifies the key validity, and generates a valid encryption key;

[0130] In actual application scenarios, for example, when vehicles exchange information through the Internet of Vehicles system, the security of the data must be confirmed first. A suitable key must be selected from a set of preset key libraries to encrypt the data, analyze the validity period of each key and its compatibility with the current communication protocol, and then select the best matching key. 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 Internet of Vehicles 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 submodule calls the valid encryption key, parses the data frame structure, identifies the encrypted data and integrity check code, and uses the formula:

[0132]

[0133] Calculate the data encryption consistency value, compare the integrity check code, and obtain the encrypted data analysis result;

[0134] Among them, C E Represents the data encryption consistency value, D enc Represents encrypted data, D key Represents the key to encrypt the 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] Calling valid encryption keys to parse data frames and identify encrypted data and integrity check codes is crucial in high-security data transmission scenarios. For example, in the communication process between autonomous vehicles, when vehicle A sends sensor data to vehicle B, the integrity and security of the data must be ensured. The data packet structure must be parsed to determine the components of the data packet, including the payload, encrypted header information, and integrity check codes. The key-encrypted data portion is extracted and compared with the original encrypted data. The data encryption consistency value is calculated to measure the consistency of data encryption and decryption.

[0136] Assume that the encrypted data D in a data packet enc is 2048, and the key encrypts the data D key For 2025, the packet security parameter S j The value range is between 0.8 and 1.2. 1 =1.1, S 2 =0.9, S 3 =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] Calculate the denominator:

[0139]

[0140] Calculate the numerator: |2048-2025|=23;

[0141] Final calculation:

[0142] Encryption consistency value C E The calculated value is 22.77, which can be compared with the set integrity check threshold. For example, if the integrity check threshold 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 calculated result exceeds 25, it means that the data has been interfered with or tampered with during transmission, and it is necessary to re-encrypt the data or request retransmission. In vehicle communication, the calculation can help vehicle B determine whether the data packet from vehicle A is credible, and finally obtain the encrypted data parsing result.

[0143] The integrity check submodule calls the encrypted data parsing result, parses the pre-shared key, calculates the integrity check value, screens the data integrity deviation range, and obtains the data integrity check 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, the encrypted data parsing result is called, and then the pre-shared key of the data receiving end is parsed to perform a final integrity check on the data. This includes using the pre-shared key to calculate the integrity check value of the data 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 during transmission. 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. By screening the data integrity deviation range, the security of the transaction is ensured, and finally a data integrity check result is generated.

[0145] See also Figure 6 , the anomaly detection module includes:

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

[0147] Comparative analysis of sending frequency and data frame size. For example, in a vehicle network, assuming that 100 frames of data should be sent per second, and the size of each frame is 1KB, if the actual sending frequency drops to 80 frames / second, or the data frame size changes to 1.2KB, it can be preliminarily determined that the data integrity is damaged. This is achieved by monitoring the network transmission efficiency and frame loss rate. The specific steps for calculating the data integrity deviation value include measuring the actual number of data frames transmitted through the network per second and comparing it with a preset benchmark to determine whether there is a deviation and the specific value of the deviation. For example, if the detected data frame size continues to exceed a predetermined threshold, it is considered that data tampering or error has occurred. By comparing the data with the set integrity benchmark value, such as comparing the actual sending frequency with the preset frequency, it is confirmed whether the deviation exceeds the allowable range. The steps together complete the data integrity assessment, determine whether there is an integrity deviation exceeding the threshold, and obtain the data integrity deviation analysis result.

[0148] The data anomaly score calculation submodule calls the data integrity deviation analysis results to calculate the anomaly score value of each data item, and uses the formula based on the integrity deviation situation:

[0149]

[0150] Get a data anomaly score set;

[0151] Among them, D s Represents the data anomaly score, F k represents the size of the kth data frame, F th Represents the benchmark value of the data frame size, L kRepresents the integrity deviation value of the kth data frame, T k represents the sending frequency of the kth data frame, A k represents the arbitration delay of the kth data frame, A th represents the base value of arbitration delay, M represents the total number of data frames;

[0152] In the vehicle CAN network, it is assumed that the data integrity deviation analysis results are as follows: the data frame size F k The value range is [0.8KB, 1.3KB], and its reference value F th is 1.0KB, and the sending frequency is T k Between 75 frames / second and 105 frames / second, the base transmission frequency T th is 100 frames / second, arbitration delay A k The base arbitration delay A is between 2ms and 12ms. th Set to 5ms;

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

[0154] F 1 =1.2KB, T 1 =80, A 1 =10ms;

[0155] F 2 =0.9KB, T 2 =95, A 2 =4ms;

[0156] F 3 =1.3KB, T 3 =70, A 3 =8ms;

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

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

[0159] L 1 =|1.2-1.0|=0.2KB;

[0160] L 2 =|0.9-1.0|=0.1KB;

[0161] L 3 =|1.3-1.0|=0.3KB;

[0162] Compute the score for each data frame:

[0163]

[0164] Final data anomaly score calculation results:

[0165] D s =D 1 +D 2 +D 3 =0.00049+0.000104+0.00125=0.001844;

[0166] The calculation obtains the anomaly score value of each data item and combines it with the integrity deviation. For example, for a specific data packet, if the deviation of its data frame size, transmission frequency and arbitration delay from the baseline value exceeds the set threshold, the anomaly score of the data packet will increase significantly, and a data anomaly score set will be obtained.

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

[0168] Filter out data items with abnormal scores that exceed the limit. For example, after the data anomaly score is calculated, all data packets that exceed the set threshold (such as a score exceeding 50 points) are marked as abnormal. Data items include data packets with abnormally low sending frequency, abnormally large data frame size, or abnormally long arbitration delay. Data with abnormal scores exceeding the set threshold are marked, and abnormal data features are extracted, including analyzing specific parameters of the data packets marked as abnormal, such as data frame size and sending time interval, to determine their abnormal patterns. This involves comparing abnormal data with normal operating data to verify the accuracy of the abnormal pattern and generating vehicle network anomaly detection and judgment results, which will directly affect network maintenance decisions 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, and includes the following steps:

[0170] S1: Based on the CAN-FD data frame information, extract the bit rate, data packet length, and number of padding bytes, calculate the data packet length adjustment parameters, adjust the data segment rate, optimize the data frame transmission, and obtain the optimized data frame parameters;

[0171] S2: Based on the optimized data frame parameters, extract the data bus traffic status, node transmission frequency, and average load value, adjust the bandwidth of low-priority nodes, optimize the transmission of high-priority nodes, and obtain the dynamic bandwidth allocation result;

[0172] S3: Based on the dynamic bandwidth allocation result, call the signal arbitration priority, analyze the signal access conflict, adjust the low priority signal competition frequency, optimize the high priority signal access window, adjust the signal frame transmission interval, optimize the bus load balance, and obtain the signal access scheduling parameters;

[0173] S4: Based on the signal access scheduling parameters, the dynamic key management rules are called to identify abnormal data frames, adjust the data encryption method, optimize the key matching, update the data integrity check, and obtain the security data integrity parameters;

[0174] S5: Based on the security data integrity parameters, extract the data frame size, transmission frequency, arbitration delay, calculate the anomaly score, adjust the priority of the abnormal data frame, filter the abnormal data, and obtain the vehicle network anomaly detection judgment result.

[0175] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An efficient vehicle network control system based on CAN-FD, characterized in that: The system comprises: The data transmission optimization module calls the vehicle control information data based on the CAN-FD data frame information, filters the transmission priority threshold, calculates the data packet length adjustment parameter, determines the proportion of padding bytes, adjusts the data segment rate, and obtains the updated data frame parameters; The bandwidth management module extracts the data bus traffic status and the node data transmission frequency based on the updated data frame parameters, calculates the bandwidth usage average, adjusts the bandwidth proportion of low priority nodes, and generates a dynamic bandwidth allocation result; The signal scheduling control module extracts the vehicle network signal priority data based on the dynamic bandwidth allocation result, determines the bus access conflict status, adjusts the non-emergency signal priority, and generates a signal priority adjustment result; The encryption security control module extracts the encryption key based on the signal priority adjustment result, identifies the encrypted data and the integrity check code, compares the data integrity verification value, and generates a data integrity check result; Based on the data integrity check result, the anomaly detection module extracts the data frame size, the transmission frequency, calculates the data anomaly score, filters the data with anomaly score exceeding the limit, and generates the vehicle network anomaly detection judgment result.

2. The efficient vehicle network control system based on CAN-FD according to claim 1 is characterized in that: The updated data frame parameters include data packet length adjustment parameters, padding byte ratio, bit rate adjustment value, and data segment rate setting; the dynamic bandwidth allocation result includes bandwidth usage average, instantaneous traffic exceeding node identifier, and low priority node bandwidth ratio setting; the signal priority adjustment result includes signal priority data, arbitration priority parameter, bus access conflict status identifier, and non-emergency signal priority adjustment value; the data integrity check result includes encryption key parameter, symmetric encryption parameter, encrypted data identifier, integrity check code value, pre-shared key, and integrity verification result; the vehicle network anomaly detection determination result includes data frame size parameter, transmission frequency value, arbitration delay time, data anomaly score value, anomaly detection algorithm, and anomaly score exceeding data identifier.

3. The efficient vehicle network control system based on CAN-FD according to claim 1 is characterized in that: The data transmission optimization module comprises: The parameter screening submodule is based on the CAN-FD data frame information, including bit rate, data packet length, number of padding bytes, vehicle control signal, status information data, and diagnostic data stream data. It screens the data stream type information, analyzes the transmission priority and sets the threshold, screens the data stream, and obtains the screened data frame parameters. The data packet length adjustment submodule calls the filtered data frame parameters, identifies the proportion of padding bytes, and determines whether it exceeds the proportion, using the formula: Calculate the optimized data packet length; Among them, 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 bit rate in the arbitration phase, and M represents the total number of data frames; The data segment rate optimization submodule calls the optimized data packet length, identifies the bit rate ratio, adjusts the data segment rate, and obtains updated data frame parameters.

4. The efficient vehicle network control system based on CAN-FD according to claim 3 is characterized in that: The bandwidth management module includes: 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 based on the updated data frame parameters; The node bandwidth allocation submodule calls the data bus traffic distribution, filters out nodes with instantaneous traffic exceeding the limit, and uses the formula: Calculate the bandwidth adjustment amount of the low priority node to obtain the node bandwidth adjustment parameter; Among them, B adj is the bandwidth adjustment amount of low priority nodes, B avg is the mean value of node bandwidth usage, F node is the current node data transmission frequency, F thr is the flow threshold, M node is the total number of node connections, U curr is the current data bus load; The dynamic bandwidth adjustment submodule calls the node bandwidth adjustment parameters, allocates bandwidth proportions of low-priority nodes according to the node bandwidth adjustment conditions, adjusts data flow priorities, and generates dynamic bandwidth allocation results.

5. The efficient vehicle network control system based on CAN-FD according to claim 4 is characterized in that: The signal scheduling control module includes: The signal priority extraction submodule extracts the vehicle network signal priority data based on the dynamic bandwidth allocation result, and calculates the initial priority value, signal type, bandwidth occupancy ratio and data transmission rate to obtain the signal priority interval characteristics; 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 impact value; The signal priority adjustment submodule reallocates the priority based on the non-emergency signal conflict impact value, using the formula: Adjusting the priority of non-emergency signals and generating signal priority adjustment results; Among them, S adj Represents the signal priority adjustment value, S ori Represents the initial priority value, C conf represents the priority conflict impact factor, B util represents bandwidth utilization, W bal Represents the bus load balancing factor, V dyn Represents the dynamic bandwidth allocation result, V thr Indicates the priority adjustment threshold.

6. The efficient vehicle network control system based on CAN-FD according to claim 5 is characterized in that: The encryption security control module includes: The key extraction submodule extracts the encryption key based on the signal priority adjustment result, parses the data frame, screens the matching symmetric encryption parameters, identifies the key validity, and generates a valid encryption key; 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: Calculate the data encryption consistency value, compare the integrity check code, and obtain the encrypted data analysis result; Among them, C E Represents the data encryption consistency value, D enc Represents encrypted data, D key Represents the key to encrypt the 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; The integrity check submodule calls the encrypted data parsing result, parses the pre-shared key, calculates the integrity check value, screens the data integrity deviation range, and obtains the data integrity check result.

7. The efficient vehicle network control system based on CAN-FD according to claim 6 is characterized in that: The anomaly detection module comprises: The data error detection and calibration submodule extracts the data frame size, transmission frequency, arbitration delay, calculates the integrity deviation value, compares the integrity reference value, and obtains the data integrity deviation analysis result based on the data integrity check result; The data anomaly score calculation submodule calls the data integrity deviation analysis results, calculates the anomaly score value of each data item, and uses the formula based on the integrity deviation situation: Get a data anomaly score set; Among them, D s Represents the data anomaly score, F k represents the size of the kth data frame, F th Represents the benchmark value of the data frame size, L k Represents the integrity deviation value of the kth data frame, T k represents the sending frequency of the kth data frame, A k represents the arbitration delay of the kth data frame, A th represents the base value of arbitration delay, M represents the total number of data frames; The abnormal data screening submodule calls the data anomaly score set, screens the over-limit abnormal score data, marks the abnormal data items, extracts abnormal features, and generates a vehicle network anomaly detection and determination result.

8. An efficient vehicle network control method based on CAN-FD, characterized in that: The high-efficiency vehicle network control system based on CAN-FD according to any one of claims 1 to 7 comprises the following steps: S1: Based on the CAN-FD data frame information, extract the bit rate, data packet length, and number of padding bytes, calculate the data packet length adjustment parameters, adjust the data segment rate, optimize the data frame transmission, and obtain the optimized data frame parameters; S2: Based on the optimized data frame parameters, extract the data bus traffic status, node transmission frequency, and average load value, adjust the bandwidth of low-priority nodes, optimize the transmission of high-priority nodes, and obtain a dynamic bandwidth allocation result; S3: Based on the dynamic bandwidth allocation result, call the signal arbitration priority, analyze the signal access conflict, adjust the low priority signal competition frequency, optimize the high priority signal access window, adjust the signal frame transmission interval, optimize the bus load balance, and obtain the signal access scheduling parameter; S4: Based on the signal access scheduling parameters, calling dynamic key management rules, identifying abnormal data frames, adjusting data encryption methods, optimizing key matching, updating data integrity check, and obtaining security data integrity parameters; S5: Based on the security data integrity parameter, extract the data frame size, transmission frequency, arbitration delay, calculate the anomaly score, adjust the priority of the abnormal data frame, filter the abnormal data, and obtain the vehicle network anomaly detection result.

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