An intelligent network connected vehicle information security risk assessment method and system

Abstract

The application is suitable for the field of intelligent networked automobile technology, and provides an intelligent networked automobile information security risk assessment method and system.The method comprises the following steps: acquiring real-time road condition summary data in a preset road section in a specific road, historical passing track data of a target vehicle, and a vehicle group target direction corresponding to a vehicle-to-vehicle temporary relay; extracting low-precision road condition information related to the dynamic of the target vehicle from the real-time road condition summary data, and determining a predicted heading consistency index and a predicted residence persistence index of the target vehicle based on the low-precision road condition information.The future driving trend and residence capacity of the vehicle can be predicted without the vehicle publicly disclosing the accurate destination or continuous track; and the predicted value is dynamically corrected by taking the historical average angle deviation proportion as a correction factor, so as to generate a predicted comprehensive dynamic score value which can comprehensively reflect the direction deviation risk, the communication interruption risk and the potential malicious relay risk.

Description

Technical Field

[0001] This invention belongs to the field of intelligent connected vehicle technology, and in particular relates to a method and system for assessing information security risks of intelligent connected vehicles. Background Technology

[0002] Existing methods for assessing information security risks in intelligent connected vehicles largely rely on precise destinations, real-time driving trajectories, or high-precision onboard sensor data directly shared between vehicles to establish trust relationships and predict potential security risks. While this approach can accurately reflect vehicle driving trends, in real-world urban roads or complex traffic scenarios, vehicles often cannot fully disclose their destinations and complete trajectories due to privacy protection, data security, or equipment compatibility concerns. Furthermore, when a fleet needs to temporarily establish vehicle-to-vehicle communication relays, the connectivity between nodes is random and short-lived. Traditional methods struggle to make timely and comprehensive assessments of relay reliability and potential information security risks for individual vehicles when data is limited and dynamically changing.

[0003] To address this situation, some existing technologies attempt to assist risk assessment by incorporating macro-level statistical data such as road traffic flow, average speed, or regional congestion index. However, most of these are merely used as reference parameters for traffic conditions, lacking precise matching analysis between target vehicles and the target direction of the vehicle group, and failing to fully utilize historical vehicle trajectory information to correct real-time prediction results. This leads to assessment results that are often lagging or one-sided, unable to quickly quantify the potential risks of target vehicles before temporary vehicle-to-vehicle relays are formed, thus affecting the stability of relay links and the overall information security of the vehicle group. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for assessing information security risks in intelligent connected vehicles, aiming to solve the problems mentioned in the background art.

[0005] This invention is implemented as follows: a method for assessing information security risks in intelligent connected vehicles, the method comprising:

[0006] Acquire real-time traffic summary data, historical travel trajectory data of target vehicles, and vehicle group target directions corresponding to vehicle-to-vehicle temporary relays within a preset road segment in a specific road;

[0007] Low-precision traffic information related to the dynamics of the target vehicle is extracted from real-time traffic summary data. Based on the low-precision traffic information, the predicted heading consistency index and predicted dwell persistence index of the target vehicle are determined. The low-precision traffic information includes parameters such as the probability distribution of road traffic flow direction, traffic flow density and road congestion index within a future preset time range.

[0008] Based on historical traffic trajectory data, the frequency and direction of the target vehicle passing through the preset road segment within the historical preset time range are obtained, and the average angle deviation ratio between the target vehicle's direction of passing through the preset road segment and the target direction of the vehicle group is used as a correction factor.

[0009] Based on the correction factor, the predicted course consistency index is corrected, and the predicted dwell duration index and the corrected predicted course consistency index are weighted to obtain the predicted comprehensive dynamic score value. The predicted comprehensive dynamic score value is used to reflect the information security risk level of the target vehicle in the temporary relay environment.

[0010] As a further limitation of the technical solution of the embodiments of the present invention, the specific road refers to a road with regular traffic flow.

[0011] As a further limitation of the technical solution of this invention, the steps of extracting low-precision traffic information related to the dynamics of the target vehicle from real-time traffic summary data, and determining the predicted heading consistency index and predicted dwell persistence index of the target vehicle based on the low-precision traffic information, wherein the low-precision traffic information includes parameters such as the probability distribution of road segment traffic flow direction, traffic flow density, and road congestion index within a future preset time range, include:

[0012] Predict the probability distribution of traffic flow direction of the target vehicle in the preset road segment within the future preset time range from real-time traffic summary data, discretize the probability distribution of traffic flow direction into several direction intervals, and obtain the traffic flow direction probability value of each direction interval.

[0013] Calculate the angle between each directional interval and the target direction of the vehicle group, and determine the consistency weight of each directional interval based on the angle.

[0014] The consistency weight of each directional interval is multiplied by its corresponding traffic flow direction probability value to obtain a weighted value. The weighted values ​​of each directional interval are then summed to obtain the predicted heading consistency index.

[0015] The traffic flow density and road congestion index of the target vehicle in the preset road segment within the future preset time range are extracted from the real-time traffic summary data. The traffic flow density is normalized to obtain the density coefficient, and the road congestion index is normalized to obtain the congestion coefficient.

[0016] The predicted dwell duration index of the target vehicle is obtained by weighting the density coefficient and the congestion coefficient.

[0017] As a further limitation of the technical solution of this invention embodiment, the step of obtaining the frequency and direction of the target vehicle's passage through a preset road segment within a preset historical time range based on historical traffic trajectory data, and using the average angular deviation ratio between the target vehicle's direction of passage through the preset road segment and the target direction of the vehicle group as a correction factor includes:

[0018] Based on historical traffic trajectory data, the frequency and direction of the target vehicle passing through the preset road segment within the preset historical time range are obtained;

[0019] The driving direction of the target vehicle each time it passes through a preset road segment is compared with the target direction of the vehicle group to obtain the angular deviation ratio between the driving direction and the target direction of the vehicle group.

[0020] The average angle deviation ratio is obtained by summing all the angle deviation ratios obtained from the comparisons and then dividing by the frequency of passage. The average angle deviation ratio is then used as a correction factor.

[0021] As a further limitation of the technical solution of this invention embodiment, based on the correction factor, the predicted heading consistency index is corrected, and the predicted dwell persistence index and the corrected predicted heading consistency index are weighted to calculate the predicted comprehensive dynamic score value. The step of using the predicted comprehensive dynamic score value to reflect the information security risk level of the target vehicle in the temporary relay environment includes:

[0022] The average angle deviation ratio is mapped to a normalized coefficient, which is used as a correction coefficient. The correction coefficient is multiplied by the predicted heading consistency index to obtain the optimized predicted heading consistency index.

[0023] The predicted dwell persistence index and the optimized predicted course consistency index are weighted and calculated to obtain the predicted comprehensive dynamic score.

[0024] The predicted comprehensive dynamic score is used to quantify the potential communication interruption risk, abnormal path deviation risk, and potential malicious relay risk of the target vehicle during vehicle-to-vehicle temporary relay, reflecting the degree of information security risk of the target vehicle in the temporary relay environment.

[0025] A smart connected vehicle information security risk assessment system, the system comprising: a data acquisition module, a prediction index calculation module, a correction factor calculation module, and a risk score generation module; wherein:

[0026] The data acquisition module is used to acquire real-time traffic summary data, historical travel trajectory data of target vehicles, and vehicle group target directions corresponding to vehicle-to-vehicle temporary relays within a preset road segment in a specific road.

[0027] The prediction index calculation module is used to extract low-precision traffic information related to the dynamics of the target vehicle from real-time traffic summary data, and to determine the predicted heading consistency index and predicted dwell persistence index of the target vehicle based on the low-precision traffic information. The low-precision traffic information includes parameters such as the probability distribution of road segment traffic flow direction, traffic flow density and road congestion index within a future preset time range.

[0028] The correction factor calculation module is used to obtain the frequency and direction of the target vehicle passing through the preset road segment within the historical preset time range based on historical traffic trajectory data, and to use the average angle deviation ratio between the target vehicle's direction of passing through the preset road segment and the target direction of the vehicle group as the correction factor.

[0029] The risk score generation module is used to correct the predicted course consistency index based on the correction factor, and to calculate the predicted comprehensive dynamic score value by weighting the predicted dwell duration index and the corrected predicted course consistency index. The predicted comprehensive dynamic score value is used to reflect the information security risk level of the target vehicle in the temporary relay environment.

[0030] As a further limitation of the technical solution of this embodiment of the invention, the prediction index calculation module specifically includes:

[0031] The direction probability distribution prediction unit is used to predict the road segment traffic flow direction probability distribution of the target vehicle in the preset road segment within a preset time range from real-time traffic summary data. The road segment traffic flow direction probability distribution is discretized into several direction intervals, and the traffic flow direction probability value of each direction interval is obtained.

[0032] The direction consistency weight calculation unit is used to calculate the direction angle between each direction interval and the target direction of the vehicle group, and determine the consistency weight of each direction interval based on the direction angle.

[0033] The predictive heading consistency index generation unit is used to multiply the consistency weight of each directional interval by its corresponding traffic flow direction probability value to obtain a weighted value, and then sum the weighted values ​​of each directional interval to obtain the predicted heading consistency index.

[0034] The traffic congestion parameter normalization unit is used to extract the traffic flow density and road congestion index of the target vehicle in the preset road segment within the future preset time range from the real-time traffic summary data, normalize the traffic flow density to obtain the density coefficient, and normalize the road congestion index to obtain the congestion coefficient.

[0035] The predicted dwell duration index generation unit is used to calculate the predicted dwell duration index of the target vehicle by weighting the density coefficient and the congestion coefficient.

[0036] As a further limitation of the technical solution of this embodiment of the invention, the correction factor calculation module specifically includes:

[0037] The historical trajectory data acquisition unit is used to acquire the frequency and direction of the target vehicle passing through a preset road segment within a preset historical time range based on historical travel trajectory data.

[0038] The angle deviation ratio calculation unit is used to compare the driving direction of the target vehicle each time it passes through the preset road segment with the target direction of the vehicle group to obtain the angle deviation ratio between the driving direction and the target direction of the vehicle group.

[0039] The average angle deviation ratio generation unit is used to add up the angle deviation ratios obtained from all comparisons and divide by the frequency of passage to obtain the average angle deviation ratio, and use the average angle deviation ratio as a correction factor.

[0040] As a further limitation of the technical solution of this embodiment of the invention, the risk scoring generation module specifically includes:

[0041] The correction coefficient generation unit is used to map the average angle deviation ratio into a normalized coefficient, which is used as the correction coefficient. The correction coefficient is multiplied by the predicted heading consistency index to obtain the optimized predicted heading consistency index.

[0042] The comprehensive score calculation unit is used to calculate the predicted comprehensive dynamic score by weighting the predicted dwell persistence index and the optimized predicted course consistency index.

[0043] The dynamic scoring application unit is used to quantify the potential communication interruption risk, abnormal path deviation risk, and potential malicious relay risk of the target vehicle during the vehicle-to-vehicle temporary relay process based on the predicted comprehensive dynamic score value, reflecting the information security risk level of the target vehicle in the temporary relay environment.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] This invention addresses the pain points of incomplete vehicle information sharing and the inability of existing assessment methods to objectively quantify individual vehicle safety in real-time in temporary vehicle-to-vehicle relay environments. It proposes a comprehensive risk assessment scheme that integrates real-time, low-precision road condition information with historical traffic trajectory data. By extracting parameters such as the probability distribution of traffic flow direction, traffic flow density, and road congestion index, a predicted heading consistency index and a predicted dwell duration index are established. This allows for the prediction of future driving trends and dwell capacity without requiring vehicles to disclose precise destinations or continuous trajectories. Furthermore, the predicted values ​​are dynamically corrected using the historical average angle deviation ratio as a correction factor, generating a comprehensive dynamic score that reflects the risks of directional deviation, communication interruption, and potential malicious relaying. This scheme can be widely applied to intelligent connected transportation scenarios with regularly occurring vehicles, such as urban roads and main roads in industrial parks. It provides a reliable basis for selecting relay nodes in vehicle groups, allocating communication resources, and isolating abnormal vehicles, helping to improve the real-time defense and security management capabilities of vehicle networks. It possesses significant technological innovation and promising industrial application prospects. Attached Figure Description

[0046] Figure 1A flowchart of the method provided in the embodiments of the present invention;

[0047] Figure 2 This is a flowchart illustrating the calculation of the prediction index in the method provided in this embodiment of the invention;

[0048] Figure 3 This is a flowchart illustrating the calculation of the correction factor in the method provided in this embodiment of the invention;

[0049] Figure 4 This is a flowchart illustrating the process of generating a risk score in the method provided in this embodiment of the invention;

[0050] Figure 5 Application architecture diagram of the system provided in the embodiments of the present invention;

[0051] Figure 6 This is a structural block diagram of the prediction index calculation module in the system provided in the embodiments of the present invention;

[0052] Figure 7 This is a structural block diagram of the correction factor calculation module in the system provided in the embodiments of the present invention;

[0053] Figure 8 This is a structural block diagram of the risk scoring generation module in the system provided in the embodiments of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0055] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0056] Specifically, a method for assessing information security risks in intelligent connected vehicles includes the following steps:

[0057] Step S100: Obtain real-time traffic summary data, historical travel trajectory data of target vehicles, and vehicle group target direction corresponding to vehicle-to-vehicle temporary relay in a preset road segment of a specific road.

[0058] In this embodiment of the invention, a specific road refers to a road with regular vehicle traffic, meaning that vehicle entry and exit are predictable within a certain time period, and stable and statistically representative historical traffic trajectory data can be accumulated. Typical examples include urban arterial roads, secondary arterial roads, trunk roads in industrial parks or industrial zones, urban expressways, and other road sections with daily commuting, high-frequency logistics, or public transportation routes. In contrast, some road sections with low traffic volume and sporadic vehicle traffic (such as remote highways, mountain roads in scenic areas, etc.) are not suitable for inclusion in the category of specific roads because it is difficult to form usable historical trajectory samples on these road sections, making it difficult to support the accurate calculation of correction factors.

[0059] A pre-defined road segment refers to a continuous road unit with clear geographical boundaries within the aforementioned specific roads, delineated according to assessment needs. It can be a segment between one intersection and the next, or a combination of several continuous road segments. Its delineation can be dynamically adjusted based on the segmentation rules of the road management system or actual relay needs, so as to effectively collect data such as the frequency of traffic and direction of travel of target vehicles.

[0060] Real-time traffic summary data refers to traffic statistics collected and released in real time within a preset time window by infrastructure such as urban traffic management platforms, roadside units (RSUs), smart streetlights, video surveillance, or high-precision geomagnetic detectors. This includes the probability distribution of traffic flow direction, traffic flow density, road congestion index, average speed, and traffic light phase change trends. This data is usually provided in aggregate form and does not involve the specific routes of individual vehicles. It can be obtained directly from the open interfaces of the city's smart transportation cloud platform or traffic control center. To ensure the timeliness and completeness of the data, the impact of communication latency, data refresh frequency, and unforeseen events (such as accidents or road closures) on data accuracy must be considered during the acquisition process.

[0061] Historical travel trajectory data refers to the travel records of a target vehicle within a preset time frame on a preset road segment, including the number of times it passed through and the corresponding direction of travel. This data can originate from vehicle-to-everything (V2X) platforms, historical traffic condition archives from traffic management departments, the vehicle's own driving recorder (such as OBU or T-BOX), or authorized third-party mobility service platforms. Because historical travel trajectory data involves vehicle location privacy, data security and privacy protection requirements must be followed when acquiring it. Methods such as anonymization, authorized interfaces, or data summarization can be used to ensure legality and compliance, and to guarantee the continuity and representativeness of the data.

[0062] Vehicle-to-vehicle temporary relay refers to a temporary vehicular communication link established by multiple connected vehicles in situations where fixed roadside infrastructure is lacking or existing communication link coverage is insufficient. This link is used to relay data transmission and maintain network connectivity within the vehicle group. The corresponding vehicle group target direction refers to the overall data transmission or travel direction that the vehicle group aims to maintain during the current relay task. Examples include forwarding warning information from an accident scene to the following traffic flow or relaying coordinated control commands from the head of the convoy to the tail. In specific urban or park road sections, vehicle-to-vehicle temporary relay often faces challenges such as complex road structures, random changes in traffic flow, and rapid entry and exit of nodes. On the one hand, the vehicle group target direction may frequently change due to traffic light phases, sudden congestion, or temporary traffic control. On the other hand, different vehicles have privacy concerns about sharing their destinations, making it difficult to know their actual travel trends in real time. This invention, by fusing low-precision real-time traffic information and historical travel trajectory data, achieves reliable prediction and correlation of the vehicle group target direction without requiring vehicles to disclose their complete travel destinations, providing a solid data foundation for subsequent information security risk assessment.

[0063] Furthermore, the method for assessing information security risks in intelligent connected vehicles also includes the following steps:

[0064] Step S200: Extract low-precision traffic information that is dynamically related to the target vehicle from the real-time traffic summary data, and determine the predicted heading consistency index and predicted dwell persistence index of the target vehicle based on the low-precision traffic information. The low-precision traffic information includes parameters such as the probability distribution of road segment traffic flow direction, traffic flow density, and road congestion index within a future preset time range.

[0065] Specifically, Figure 2 A flowchart for calculating the forecast index is shown.

[0066] The process involves extracting low-precision traffic information related to the target vehicle's dynamics from real-time traffic summary data, and determining the target vehicle's predicted heading consistency index and predicted dwell persistence index based on this low-precision traffic information. This low-precision traffic information includes parameters such as the probability distribution of road segment traffic flow direction, traffic flow density, and road congestion index within a future preset time range. Specifically, this includes the following steps:

[0067] Step S201: Predict the probability distribution of traffic flow direction of the target vehicle in the preset road segment within the future preset time range from the real-time traffic summary data, discretize the probability distribution of traffic flow direction into several direction intervals, and obtain the traffic flow direction probability value of each direction interval.

[0068] Step S202: Calculate the angle between each directional interval and the target direction of the vehicle group, and determine the consistency weight of each directional interval based on the angle.

[0069] Step S203: Multiply the consistency weight of each directional interval by its corresponding traffic flow direction probability value to obtain a weighted value, and sum the weighted values ​​of each directional interval to obtain the summed result as the predicted heading consistency index.

[0070] Step S204: Extract the traffic flow density and road congestion index of the target vehicle's location on the preset road segment within the future preset time range from the real-time traffic summary data, normalize the traffic flow density to obtain the density coefficient, and normalize the road congestion index to obtain the congestion coefficient.

[0071] Step S205: The predicted dwell persistence index of the target vehicle is calculated by weighting the density coefficient and the congestion coefficient.

[0072] In this embodiment of the invention, low-precision traffic information refers to statistical and aggregated data that reflects the overall traffic operation status of a road without involving the precise location and real-time trajectory of individual vehicles. Examples include the probability distribution of traffic flow direction, traffic flow density, road congestion index, average vehicle speed, traffic light phase change trends, and meteorological conditions. This type of information typically originates from traffic management cloud platforms, roadside units (RSUs), or urban intelligent transportation systems. After temporal and spatial aggregation and anonymization processing, it features wide coverage, low privacy risk, and rapid accessibility, meeting the dual requirements of intelligent connected vehicles for data compliance and real-time performance.

[0073] In this application, only "traffic flow density" and "road congestion index" are selected as the calculation basis for the predicted dwell duration index, mainly based on the following considerations: First, the predicted dwell duration index is used to measure the probability that target vehicles will maintain a stable connection and continuous dwelling on a preset road segment within a preset time range in the future. Whether vehicles will stay for a long time or travel at low speed is directly related to the traffic flow density and congestion status of the road segment. Traffic flow density reflects the real-time traffic volume per unit road segment and is a key parameter for judging the road saturation level; the road congestion index integrates factors such as vehicle speed, traffic flow, and queue length, and can quantify the congestion and dwell level of the road. Both together determine the probability of vehicles dwelling on that road segment. Second, compared with other low-precision parameters such as average vehicle speed, weather conditions, or traffic light phase changes, traffic flow density and road congestion index have a stronger direct correlation with the actual dwell time of vehicles and the continuity of relay links, and the data acquisition is mature, stable, and reliable. By focusing on these two core metrics, we can simplify the calculation model while ensuring the accuracy of predictions, improve real-time response capabilities and the feasibility of system implementation, thereby meeting the real-time and reliability requirements for risk assessment in vehicle-to-vehicle temporary relay scenarios.

[0074] The probability distribution of traffic flow direction on a road segment refers to the probability distribution of traffic flow in each possible direction within a predetermined road segment within a future preset time range. It can be generated by real-time statistical models from traffic monitoring systems, roadside units (RSUs), and intelligent transportation cloud platforms. The probability value of traffic flow direction in each direction represents the probability of a group of vehicles traveling within that direction interval, providing a quantitative expression of traffic trends over a short period. Discretizing the probability distribution of traffic flow direction on a road segment into several direction intervals means dividing the continuous directional space into a finite number of discrete angle intervals, such as every 10 or 15 degrees, centered on the target direction of the vehicle group or with a 360-degree radius. The significance of discretization lies in two aspects: firstly, it facilitates subsequent calculation of the probability value and consistency weight of each direction interval; secondly, it preserves key trend features without requiring excessively high resolution, thus balancing data processing volume and real-time performance.

[0075] The directional angle refers to the angular difference between the center direction of each directional interval and the target direction of the vehicle group. In step S202, by calculating the directional angle, the degree of deviation between each directional interval and the target direction of the vehicle group can be quantified. The system determines the consistency weight of each directional interval based on this angle, for example, a linear function can be used. or cosine function This approach assigns higher weights to intervals whose direction is closer to the target direction of the vehicle group. The significance of using the directional angle to determine the consistency weight lies in ensuring that the calculation results accurately reflect the convergence between the traffic flow direction distribution and the target direction of the vehicle group. This helps identify traffic components more likely to travel along the vehicle group's direction within future time windows, thereby improving the accuracy of risk assessment.

[0076] The predicted heading consistency index is a weighted cumulative result obtained by comprehensively considering the probability distribution of traffic flow direction on road segments and the consistency weight of each direction interval. It is used to quantify the degree of consistency between the predicted driving direction of a target vehicle and the target direction of the vehicle group within a preset future time range. The higher the index value, the more consistent the future driving trend of the target vehicle is with the target direction of the vehicle group, and the stronger the reliability of joining or maintaining the vehicle group relay. The predicted heading consistency index is of core significance in the information security risk assessment method of this invention: in the environment of temporary vehicle-to-vehicle relay, vehicles are often unwilling to disclose their precise destination or complete trajectory. This index can predict the reliability of the vehicle's driving direction without obtaining sensitive data. Combined with subsequent correction factors and dwell persistence indicators, the communication interruption risk, abnormal path deviation risk, and potential malicious relay risk of the target vehicle during the relay process can be comprehensively quantified, providing a scientific basis for relay node selection, bandwidth allocation, and anomaly isolation.

[0077] Furthermore, the method for assessing information security risks in intelligent connected vehicles also includes the following steps:

[0078] Step S300: Based on historical traffic trajectory data, obtain the frequency and direction of the target vehicle passing through the preset road segment within the historical preset time range, and use the average angle deviation ratio between the target vehicle's direction of travel through the preset road segment and the target direction of the vehicle group as a correction factor.

[0079] Specifically, Figure 3 A flowchart for calculating the correction factor is shown.

[0080] The process of obtaining the frequency and direction of the target vehicle's passage through a preset road segment within a preset time range based on historical traffic trajectory data, and using the average angular deviation ratio between the target vehicle's direction of passage through the preset road segment and the target direction of the vehicle group as a correction factor, specifically includes the following steps:

[0081] Step S301: Based on historical traffic trajectory data, obtain the frequency of the target vehicle passing through the preset road segment and the direction of travel within the historical preset time range;

[0082] Step S302: Compare the driving direction of the target vehicle each time it passes through the preset road segment with the target direction of the vehicle group to obtain the angular deviation ratio between the driving direction and the target direction of the vehicle group.

[0083] Step S303: Add up the angle deviation ratios obtained from all comparisons and divide by the frequency of passage to obtain the average angle deviation ratio, and use the average angle deviation ratio as a correction factor.

[0084] In this embodiment of the invention, the system first retrieves the historical travel trajectory data of the target vehicle from the historical road condition archives of the traffic management department, the vehicle-to-everything (V2X) cloud platform, or the vehicle's own driving recorder, and counts the number of times the vehicle passed through the preset road segment within a preset historical time range, as well as the specific direction of travel for each passage. This preset historical time range can be set to several days, weeks, or months according to the evaluation requirements to ensure both a sufficient sample size and the ability to reflect current travel habits.

[0085] In step S302, the system compares the direction of travel of the target vehicle each time it passes through a preset road segment with the target direction of the vehicle group, calculates the angle between the two directions, and determines the ratio of this angle to 180° as the angle deviation ratio for one passage. This ratio directly reflects the degree of deviation between the actual travel direction of the target vehicle and the target direction of the vehicle group in each historical passage, and can be used as a quantitative indicator of its historical consistency.

[0086] In step S303, the system sums all historical angular deviation ratios and divides them by the corresponding frequency of passage to obtain the average angular deviation ratio, which is then determined as a correction factor. The significance of the average angular deviation ratio as a correction factor lies in its ability to comprehensively characterize the overall consistency of a target vehicle relative to the target direction of the vehicle group over a long historical period. If a vehicle's historical travel direction on this road segment is highly consistent with the target direction of the vehicle group, the average angular deviation ratio is small, and the correction magnitude for the predicted heading consistency index is correspondingly small, indicating that it is likely to continue traveling along the target direction of the vehicle group in the future. Conversely, if historical deviations are frequent, the average angular deviation ratio is large, and the reduction effect of the correction factor on the predicted value is more significant, effectively avoiding overestimation due to short-term road condition randomness. By introducing this correction factor, this invention can dynamically correct real-time prediction results based on historical big data without relying on the precise destination of shared vehicles in real time, improving the stability and reliability of the predicted heading consistency index, thereby making the final generated comprehensive dynamic prediction score more scientific, objective, and valuable for reference.

[0087] Furthermore, the method for assessing information security risks in intelligent connected vehicles also includes the following steps:

[0088] Step S400: Based on the correction factor, the predicted heading consistency index is corrected, and the predicted dwell persistence index and the corrected predicted heading consistency index are weighted to obtain the predicted comprehensive dynamic score value. The predicted comprehensive dynamic score value is used to reflect the information security risk level of the target vehicle in the temporary relay environment.

[0089] Specifically, Figure 4 A flowchart for generating risk scores is shown.

[0090] Specifically, the predicted heading consistency index is corrected based on a correction factor. The predicted dwell time persistence index and the corrected predicted heading consistency index are then weighted to obtain a comprehensive dynamic score. This comprehensive dynamic score reflects the information security risk level of the target vehicle in a temporary relay environment. The process includes the following steps:

[0091] Step S401: Map the average angle deviation ratio to a normalization coefficient, which is used as a correction coefficient. Multiply the correction coefficient by the predicted heading consistency index to obtain the optimized predicted heading consistency index.

[0092] Step S402: The predicted dwell persistence index and the optimized predicted course consistency index are weighted and calculated to obtain the predicted comprehensive dynamic score.

[0093] Step S403: Based on the predicted comprehensive dynamic score value, quantify the potential communication interruption risk, abnormal path deviation risk, and potential malicious relay risk of the target vehicle during the vehicle-to-vehicle temporary relay process, reflecting the degree of information security risk of the target vehicle in the temporary relay environment.

[0094] In this embodiment of the invention, in step S401, the average angle deviation ratio is mapped to a normalization coefficient. This aims to ensure that the deviation results calculated from historical data are consistent in dimension with the real-time prediction indicators and facilitate subsequent weighting. The mapping can be linear or nonlinear, for example, using... or The functions are equal, where This represents the average angular deviation ratio. This is the adjustment coefficient. Through this mapping, the average angle deviation ratio, originally ranging from 0 to 1, can be transformed into a correction coefficient within the same range that is positively correlated with consistency. Multiplying this correction coefficient by the predicted heading consistency index yields the optimized predicted heading consistency index. This allows for dynamic correction of real-time prediction results using long-term historical trends, avoiding assessment distortions caused by short-term traffic fluctuations or data gaps.

[0095] In step S402, the predicted dwell persistence index and the optimized predicted heading consistency index are weighted and calculated to obtain a comprehensive dynamic score. The significance of this weighted calculation lies in unifying the two complementary risk dimensions, "direction consistency" and "dwelling capability," into a quantifiable comprehensive indicator. Direction consistency primarily measures the convergence of the target vehicle's future travel direction with the target direction of the vehicle group, while dwell persistence measures its ability to maintain a stable connection within the relay segment. Together, they determine the likelihood of a vehicle acting as a reliable node in a vehicle-to-vehicle temporary relay. Through weighted fusion, different weights can be assigned to the importance of each index according to actual applications, thereby obtaining a quantitative result that better reflects the actual risk characteristics.

[0096] In step S403, the predicted comprehensive dynamic score is used as the final output indicator to quantify the potential communication interruption risk, abnormal path deviation risk, and potential malicious relay risk of the target vehicle during vehicle-to-vehicle temporary relay. It also intuitively reflects the degree of information security risk in the temporary relay environment. A higher score indicates stronger stability in the target vehicle's driving direction and sustained dwell capability, and lower potential risks; conversely, a lower score indicates a higher level of security risk. Through this comprehensive indicator, the system can scientifically predict future risks even when vehicles have not yet shared precise destinations. This provides real-time, quantitative decision-making basis for relay node selection, communication resource scheduling, and abnormal vehicle isolation within the vehicle network, thereby significantly improving the overall security protection capability of vehicle-to-vehicle temporary relay in the Internet of Vehicles.

[0097] Furthermore, Figure 5 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0098] In another preferred embodiment of the present invention, an intelligent connected vehicle information security risk assessment system includes:

[0099] The data acquisition module 100 is used to acquire real-time traffic summary data, historical travel trajectory data of target vehicles, and vehicle group target directions corresponding to vehicle-to-vehicle temporary relays within a preset road segment in a specific road.

[0100] Furthermore, the intelligent connected vehicle information security risk assessment system also includes:

[0101] The prediction index calculation module 200 is used to extract low-precision traffic information related to the target vehicle's dynamics from real-time traffic summary data, and to determine the target vehicle's predicted heading consistency index and predicted dwell persistence index based on the low-precision traffic information. The low-precision traffic information includes parameters such as the probability distribution of road segment traffic flow direction, traffic flow density, and road congestion index within a future preset time range.

[0102] Specifically, Figure 6 The diagram shows a structural block diagram of the prediction index calculation module 200 in the system provided by an embodiment of the present invention.

[0103] In a preferred embodiment provided by the present invention, the prediction index calculation module 200 specifically includes:

[0104] The direction probability distribution prediction unit 201 is used to predict the road traffic flow direction probability distribution of the target vehicle in the preset road segment within a future preset time range from real-time traffic summary data, and to discretize the road traffic flow direction probability distribution into several direction intervals to obtain the traffic flow direction probability value of each direction interval.

[0105] The direction consistency weight calculation unit 202 is used to calculate the direction angle between each direction interval and the target direction of the vehicle group, and determine the consistency weight of each direction interval based on the direction angle.

[0106] The predictive heading consistency index generation unit 203 is used to multiply the consistency weight of each directional interval by its corresponding traffic flow direction probability value to obtain a weighted value, and then sum the weighted values ​​of each directional interval to obtain the predicted heading consistency index.

[0107] Traffic congestion parameter normalization unit 204 is used to extract the traffic flow density and road congestion index of the target vehicle in the preset road segment within the future preset time range from the real-time traffic summary data, normalize the traffic flow density to obtain the density coefficient, and normalize the road congestion index to obtain the congestion coefficient.

[0108] The predicted dwell persistence index generation unit 205 is used to calculate the predicted dwell persistence index of the target vehicle by weighting the density coefficient and the congestion coefficient.

[0109] Furthermore, the intelligent connected vehicle information security risk assessment system also includes:

[0110] The correction factor calculation module 300 is used to obtain the frequency and direction of the target vehicle passing through the preset road segment within the historical preset time range based on historical traffic trajectory data, and to use the average angular deviation ratio between the target vehicle's direction of passing through the preset road segment and the target direction of the vehicle group as the correction factor.

[0111] Specifically, Figure 7 The diagram shows a structural block diagram of the correction factor calculation module 300 in the system provided in an embodiment of the present invention.

[0112] In a preferred embodiment provided by the present invention, the correction factor calculation module 300 specifically includes:

[0113] The historical trajectory data acquisition unit 301 is used to acquire the frequency and direction of the target vehicle passing through a preset road segment within a preset historical time range based on historical traffic trajectory data.

[0114] The angle deviation ratio calculation unit 302 is used to compare the driving direction of the target vehicle each time it passes through the preset road section with the target direction of the vehicle group to obtain the angle deviation ratio between the driving direction and the target direction of the vehicle group.

[0115] The average angle deviation ratio generation unit 303 is used to add up all the angle deviation ratios obtained from the comparison and divide by the frequency of passage to obtain the average angle deviation ratio, and use the average angle deviation ratio as a correction factor.

[0116] Furthermore, the intelligent connected vehicle information security risk assessment system also includes:

[0117] The risk score generation module 400 is used to correct the predicted course consistency index based on the correction factor, and to calculate the predicted comprehensive dynamic score value by weighting the predicted dwell duration index and the corrected predicted course consistency index. The predicted comprehensive dynamic score value is used to reflect the information security risk level of the target vehicle in the temporary relay environment.

[0118] Specifically, Figure 8 The diagram shows a structural block diagram of the risk scoring generation module 400 in the system provided in an embodiment of the present invention.

[0119] In a preferred embodiment provided by the present invention, the risk scoring generation module 400 specifically includes:

[0120] The correction coefficient generation unit 401 is used to map the average angle deviation ratio into a normalized coefficient, which is used as a correction coefficient. The correction coefficient is multiplied by the predicted heading consistency index to obtain the optimized predicted heading consistency index.

[0121] The comprehensive score calculation unit 402 is used to perform weighted calculation of the predicted dwell persistence index and the optimized predicted course consistency index to obtain the predicted comprehensive dynamic score value.

[0122] The dynamic scoring application unit 403 is used to quantify the potential communication interruption risk, abnormal path deviation risk, and potential malicious relay risk of the target vehicle during the vehicle-to-vehicle temporary relay process based on the predicted comprehensive dynamic score value, reflecting the information security risk level of the target vehicle in the temporary relay environment.

[0123] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0124] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0125] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0127] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent connected vehicle information security risk assessment, characterized in that, The method comprises: acquiring real-time road condition summary data in a preset road section in a specific road, historical passing trajectory data of a target vehicle, and a target direction of a vehicle group corresponding to a temporary relay; extracting low-precision road condition information related to the dynamic of the target vehicle from the real-time road condition summary data, and determining a predicted heading consistency index and a predicted residence persistence index of the target vehicle based on the low-precision road condition information, wherein the low-precision road condition information comprises parameters such as road section traffic flow direction probability distribution, traffic flow density, and road congestion index within a future preset time range; based on the historical passing trajectory data, acquiring the passing frequency and driving direction of the target vehicle in the preset road section within a historical preset time range, and taking the average angle deviation proportion of the driving direction of the target vehicle passing through the preset road section and the target direction of the vehicle group as a correction factor; based on the correction factor, correcting the predicted heading consistency index, and calculating a predicted comprehensive dynamic score value by weighting the predicted residence persistence index and the corrected predicted heading consistency index, wherein the predicted comprehensive dynamic score value is used to reflect the information security risk degree of the target vehicle in the temporary relay environment. 2.The intelligent connected vehicle information security risk assessment method of claim 1, wherein, The specific road refers to a road with regular passing vehicles. 3.The intelligent connected vehicle information security risk assessment method of claim 1, wherein, The steps of extracting low-precision road condition information related to the dynamic of the target vehicle from the real-time road condition summary data, and determining a predicted heading consistency index and a predicted residence persistence index of the target vehicle based on the low-precision road condition information, wherein the low-precision road condition information comprises parameters such as road section traffic flow direction probability distribution, traffic flow density, and road congestion index within a future preset time range, comprise: predicting the road section traffic flow direction probability distribution of the target vehicle in the preset road section within a future preset time range from the real-time road condition summary data, discretizing the road section traffic flow direction probability distribution into several direction intervals, and acquiring the traffic flow direction probability value of each direction interval; calculating the direction angle of each direction interval with the target direction of the vehicle group, and determining the consistency weight of each direction interval according to the direction angle; multiplying the consistency weight of each direction interval with the corresponding traffic flow direction probability value to obtain a weighted value, and accumulating the weighted values of all direction intervals, and the obtained accumulation result is the predicted heading consistency index; extracting the traffic flow density and road congestion index of the target vehicle in the preset road section within a future preset time range from the real-time road condition summary data, normalizing the traffic flow density to obtain a density coefficient, and normalizing the road congestion index to obtain a congestion coefficient; weighting the density coefficient and the congestion coefficient to obtain the predicted residence persistence index of the target vehicle. 4.The intelligent connected vehicle information security risk assessment method of claim 1, wherein, The steps of acquiring the passing frequency and driving direction of the target vehicle in the preset road section within a historical preset time range based on the historical passing trajectory data, and taking the average angle deviation proportion of the driving direction of the target vehicle passing through the preset road section and the target direction of the vehicle group as a correction factor, comprise: acquiring the passing frequency and driving direction of the target vehicle in the preset road section within a historical preset time range based on the historical passing trajectory data; comparing the driving direction of the target vehicle passing through the preset road section each time with the target direction of the vehicle group to obtain the angle deviation proportion of the driving direction and the target direction of the vehicle group; Add all the angle deviation proportions obtained by comparison and divide by the frequency to obtain the average angle deviation proportion, and take the average angle deviation proportion as the correction factor. 5.The intelligent connected vehicle information security risk assessment method of claim 3, wherein, The step of correcting the predicted heading consistency index based on the correction factor, and calculating the predicted comprehensive dynamic score value by weighting the predicted residence persistence index and the corrected predicted heading consistency index, the predicted comprehensive dynamic score value is used to reflect the information security risk degree of the target vehicle in the temporary relay environment, comprises: Map the average angle deviation proportion to a normalized coefficient, which is used as a correction coefficient, multiply the correction coefficient and the predicted heading consistency index to obtain an optimized predicted heading consistency index; Calculate the predicted comprehensive dynamic score value by weighting the predicted residence persistence index and the optimized predicted heading consistency index; The predicted comprehensive dynamic score value is used to quantify the communication interruption risk, abnormal path deviation risk and potential malicious relay risk that may exist in the vehicle-to-vehicle temporary relay process of the target vehicle, and reflects the information security risk degree of the target vehicle in the temporary relay environment.

6. An intelligent connected vehicle information security risk assessment system, characterized in that, The system comprises a data acquisition module, a prediction index calculation module, a correction factor calculation module and a risk score generation module; wherein: The data acquisition module is used to acquire real-time road condition summary data in a preset road section in a specific road, historical passing trajectory data of the target vehicle, and a vehicle group target direction corresponding to vehicle-to-vehicle temporary relay; The prediction index calculation module is used to extract low-precision road condition information related to the dynamic of the target vehicle from the real-time road condition summary data, and determine the predicted heading consistency index and the predicted residence persistence index of the target vehicle based on the low-precision road condition information, the low-precision road condition information including road section traffic direction probability distribution, traffic flow density and road congestion index in a future preset time range; The correction factor calculation module is used to acquire the frequency and driving direction of the target vehicle in the preset road section in a historical preset time range based on the historical passing trajectory data, and take the average angle deviation proportion between the driving direction of the target vehicle passing the preset road section and the vehicle group target direction as the correction factor; The risk score generation module is used to correct the predicted heading consistency index based on the correction factor, and calculate the predicted comprehensive dynamic score value by weighting the predicted residence persistence index and the corrected predicted heading consistency index, the predicted comprehensive dynamic score value is used to reflect the information security risk degree of the target vehicle in the temporary relay environment.

7. The intelligent connected vehicle information security risk assessment system of claim 6, wherein, The prediction index calculation module specifically comprises: A direction probability distribution prediction unit is used to predict the road section traffic direction probability distribution of the target vehicle in the preset road section in a future preset time range from the real-time road condition summary data, and discretize the road section traffic direction probability distribution into several direction intervals to obtain the traffic direction probability value of each direction interval; A direction consistency weight calculation unit is used to calculate the direction included angle between each direction interval and the vehicle group target direction, and determine the consistency weight of each direction interval according to the direction included angle; The prediction heading consistency index generation unit is configured to multiply the consistency weight of each direction interval by the corresponding traffic flow direction probability value to obtain a weighted value, and to accumulate the weighted values of the direction intervals, and the accumulated result is the prediction heading consistency index; The traffic congestion parameter normalization unit is configured to extract the traffic flow density and road congestion index of the preset road segment where the target vehicle is located within a future preset time range from the real-time traffic summary data, normalize the traffic flow density to obtain a density coefficient, and normalize the road congestion index to obtain a congestion coefficient; The prediction residence persistence index generation unit is configured to calculate the prediction residence persistence index of the target vehicle by weighting the density coefficient and the congestion coefficient. 8.The intelligent networked vehicle information security risk assessment system of claim 6, wherein, The correction factor calculation module specifically includes: The historical trajectory data acquisition unit is configured to acquire the passing frequency and driving direction of the target vehicle on the preset road segment within a historical preset time range based on historical passing trajectory data; The angle deviation proportion calculation unit is configured to compare the driving direction of the target vehicle each time passing the preset road segment with the target direction of the vehicle group to obtain the angle deviation proportion of the driving direction and the target direction of the vehicle group; The average angle deviation proportion generation unit is configured to add all the angle deviation proportions obtained by comparison and divide the sum by the passing frequency to obtain the average angle deviation proportion, and take the average angle deviation proportion as the correction factor. 9.The intelligent networked vehicle information security risk assessment system of claim 6, wherein, The risk score generation module specifically includes: The correction coefficient generation unit is configured to map the average angle deviation proportion to a normalized coefficient, which is taken as the correction coefficient, multiply the correction coefficient and the prediction heading consistency index to obtain an optimized prediction heading consistency index; The comprehensive score calculation unit is configured to calculate the prediction residence persistence index and the optimized prediction heading consistency index by weighting to obtain a prediction comprehensive dynamic score value; The dynamic score application unit is configured to quantify the communication interruption risk, abnormal path deviation risk and potential malicious relay risk that may exist in the vehicle-to-vehicle temporary relay process of the target vehicle based on the prediction comprehensive dynamic score value, and reflect the information security risk degree of the target vehicle in the temporary relay environment.

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