Vehicle data risk scoring method and device, equipment and storage medium

Abstract

The invention discloses a risk scoring method and device for vehicle data, equipment and a storage medium. When the method provided by the embodiment of the invention is executed, the vehicle data of the target vehicle is collected, and the vehicle data is divided according to the vehicle data category, so that a plurality of vehicle data sets are obtained, and the privacy data set is identified. Then, according to the corresponding relation between the data life cycle of the target vehicle and the category label of the vehicle data set, the target data set is determined, and the data life cycle comprises a plurality of data processing links; and in combination with the target data set and the privacy data set corresponding to each processing link, carrying out risk analysis to obtain a risk value of each link. And finally, based on preset weights of different stages in the data life cycle, weighted summation is carried out on the risk values of all the processing links, and a data risk score of the target vehicle is obtained. According to the method and the device, a more objective and accurate risk assessment result is obtained through risk analysis based on data category division and life cycle stages and by adopting weighted summation to calculate the risk score.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle data processing, and in particular to a method, device, equipment and storage medium for risk scoring of vehicle data. Background Art

[0002] With the rapid development of intelligent connected vehicles and the continuous upgrading of Internet of Vehicles technology, the number of intelligent connected vehicles produced has increased rapidly, and the external communication interfaces have also been expanding. Although this complex data processing activity has brought unprecedented intelligence and convenience to vehicles, it has also brought unprecedented security risks. The communication connection between vehicles, roads and clouds constitutes an intricate digital network, and the data interaction between various terminals is close, resulting in increasingly prominent data security issues.

[0003] In order to ensure the security of data processing activities of intelligent connected vehicles, comprehensive risk assessment and management are required from the perspective of the entire life cycle. In the process of automobile data processing, once the confidentiality, integrity and availability of data are damaged, it may lead to data leakage, theft, tampering, damage, loss or illegal use, which will have a serious impact on public interests, personal rights and interests, organizational compliance rights and interests, etc. In addition, the risks of loss, leakage, tampering and abuse of intelligent connected vehicle data not only involve compliance challenges, but also involve the core issues of technical security. Therefore, it is very important to carry out risk assessment of intelligent connected vehicle data in order to timely discover and respond to potential security risks. However, the current data security risk assessment methods for the automotive industry are still imperfect, and the high dependence of intelligent connected vehicles on data makes them face many challenges.

[0004] Therefore, how to provide an accurate and objective risk assessment method for the entire life cycle of automotive data security is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] Based on the above problems, the present application provides a vehicle data risk scoring method, device, equipment and storage medium, which can accurately and objectively perform risk assessment on the entire life cycle of automobile data security.

[0006] The embodiments of the present application disclose the following technical solutions:

[0007] A risk scoring method for vehicle data, the method comprising:

[0008] Collecting vehicle data of the target vehicle;

[0009] Classifying the vehicle data based on vehicle data categories to obtain multiple vehicle data sets, and determining a privacy data set in each of the vehicle data sets; each vehicle data set is annotated with a different vehicle data category label;

[0010] Based on the correspondence between the data life cycle of the target vehicle and the vehicle data category label annotated by the vehicle data set, the vehicle data set corresponding to the data life cycle of the target vehicle is determined as the target data set; the data life cycle includes multiple data processing links;

[0011] Based on the target data set corresponding to each data processing link and the privacy data set in the target data set, risk analysis is performed on each data processing link of the target vehicle to obtain a risk value of each data processing link;

[0012] The risk value of each data processing link is weighted and summed based on the preset weight of each data life cycle to obtain the data risk score of the target vehicle.

[0013] In a possible implementation, the risk analysis of each data processing link of the target vehicle based on the target data set corresponding to each data processing link and the privacy data set in the target data set to obtain the risk value of each data processing link includes:

[0014] Determine a plurality of risk events corresponding to the data processing link;

[0015] Determining a scoring weight for each of the risk events based on the target data set corresponding to the data processing link and a privacy data set in the target data set;

[0016] Based on the experience score of each risk event, a weighted sum is performed on multiple risk events corresponding to the data processing link to obtain the risk value of the data processing link.

[0017] In a possible implementation, the classifying the vehicle data based on the vehicle data category to obtain a plurality of vehicle data sets includes:

[0018] Identify the vehicle data collection method and collection environment; the vehicle data category has a corresponding relationship with the collection method and the collection environment;

[0019] Determining the vehicle data category of the vehicle data based on the correspondence between the vehicle data category and the collection method and the collection environment;

[0020] The vehicle data having the same vehicle data category are grouped into the same vehicle data set.

[0021] In a possible implementation, the vehicle data category labels include an external vehicle data label, a vehicle cabin data label, a vehicle operation data label, and a vehicle location trajectory data label.

[0022] In a possible implementation, the data processing link includes any one of a data collection link, a data transmission link, a data storage link, a data use link, a data sharing link, and a data destruction link.

[0023] In a possible implementation, the privacy data set includes a personal general information data set, a personal important information data set, a personal sensitive information data set, and an important data information data set.

[0024] A risk scoring device for vehicle data, the device comprising:

[0025] A vehicle data collection unit, used to collect vehicle data of a target vehicle;

[0026] A first data division unit, configured to classify the vehicle data based on vehicle data categories to obtain a plurality of vehicle data sets;

[0027] A privacy data set determination unit, used to determine a privacy data set in each of the vehicle data sets; each vehicle data set is annotated with a different vehicle data category label;

[0028] A target data set determining unit, configured to determine the vehicle data set corresponding to the data life cycle of the target vehicle as the target data set based on the correspondence between the data life cycle of the target vehicle and the vehicle data category label annotated by the vehicle data set; the data life cycle includes a plurality of data processing links;

[0029] A risk analysis unit, configured to perform risk analysis on each data processing link of the target vehicle based on the target data set corresponding to each data processing link and the privacy data set in the target data set to obtain a risk value of each data processing link;

[0030] The first calculation unit is used to perform weighted summation on the risk values ​​of each data processing link based on the preset weights of each data life cycle to obtain the data risk score of the target vehicle.

[0031] In a possible implementation, the risk analysis unit specifically includes:

[0032] A risk event determination unit, used to determine a plurality of risk events corresponding to the data processing link;

[0033] A scoring weight determination unit, configured to determine a scoring weight for each of the risk events based on the target data set corresponding to the data processing link and a privacy data set in the target data set;

[0034] The second calculation unit is used to perform weighted summation on multiple risk events corresponding to the data processing link based on the experience score of each risk event to obtain the risk value of the data processing link.

[0035] A vehicle data risk scoring device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the vehicle data risk scoring method as described above is implemented.

[0036] A computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the risk scoring method for vehicle data as described above.

[0037] Compared with the prior art, this application has the following beneficial effects:

[0038] The present application provides a method, device, equipment and storage medium for risk scoring of vehicle data. Specifically, when executing the risk scoring method for vehicle data provided in the embodiments of the present application, the vehicle data of the target vehicle can be first collected, and the vehicle data can be classified based on the vehicle data category to obtain multiple vehicle data sets, and the privacy data set in each of the vehicle data sets can be determined. Then, based on the correspondence between the data life cycle of the target vehicle and the vehicle data category label annotated by the vehicle data set, the vehicle data set corresponding to the data life cycle of the target vehicle is determined as the target data set. Then, based on the target data set corresponding to each data processing link and the privacy data set in the target data set, each data processing link in the data life cycle of the target vehicle is risk analyzed to obtain the risk value of each data processing link. Finally, the risk value of each data processing link is weighted and summed based on the preset weight of each data life cycle to obtain the data risk score of the target vehicle.

[0039] This application provides more objective and accurate risk assessment results based on risk analysis of data classification and life cycle stages, as well as weighted summation to calculate risk scores. This helps to eliminate the bias caused by subjective judgment and improve the credibility of the assessment results. At the same time, by conducting a detailed risk analysis of each data processing link and calculating the risk value, the security risks of each link can be better identified and quantified, which helps to discover potential security vulnerabilities and take timely measures. In addition, by setting preset weights for different data processing links and performing weighted summation, a comprehensive risk score can be obtained. This application can adjust the weights according to actual conditions to make risk assessment more flexible and in line with specific needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 A method flow chart of a risk scoring method for vehicle data provided in an embodiment of the present application;

[0042] Figure 2 A method flow chart of a method for dividing a vehicle data set provided in an embodiment of the present application;

[0043] Figure 3 A method flow chart of a method for obtaining a risk value in a data processing link provided in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of the structure of a vehicle data risk scoring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] To facilitate understanding of the technical solutions provided by the embodiments of the present application, the background technology involved in the embodiments of the present application will be described below.

[0046] With the rapid development of intelligent connected vehicles and Internet of Vehicles technology, production has increased rapidly and communication interfaces have expanded. Although it provides unprecedented intelligent convenience, it also brings unprecedented security risks. The intricate digital network between vehicles constitutes the communication connection between the vehicle, the road and the cloud, and the data interaction is close, leading to increasingly prominent data security issues. In order to ensure the security of data processing in intelligent connected vehicles, comprehensive lifecycle risk assessment and management are imperative. Damage to data confidentiality, integrity or availability may lead to leakage, theft, tampering, destruction and other serious consequences that affect public interests, personal rights and interests, and organizational compliance rights. In response to such potential security risks, it is crucial to conduct data risk assessment of intelligent connected vehicles. However, the current data security risk assessment methods for the automotive field are still imperfect, and the data dependence of intelligent connected vehicles makes them face multiple challenges.

[0047] In order to solve this problem, a method, device, equipment and storage medium for risk scoring of vehicle data are provided in the embodiment of the present application. First, the vehicle data of the target vehicle is collected, and the data are classified based on the vehicle data category to obtain multiple vehicle data sets, and the privacy data set in each data set is determined, and different category labels are marked for each data set. Next, according to the correspondence between the data life cycle of the target vehicle and these vehicle data category labels, the vehicle data set corresponding to the data life cycle is determined as the target data set, wherein the data life cycle includes multiple data processing links. Then, based on the target data set and its privacy data set corresponding to each data processing link, a detailed risk analysis is performed on each data processing link to calculate the risk value of each link. Finally, by setting preset weights for different data life cycle stages and weighted summing the risk values ​​of each data processing link, the data risk score of the target vehicle is finally obtained. The present application provides a more objective and accurate risk assessment method by calculating the risk score based on the risk analysis and weighted summation of data category division and life cycle stage, thereby reducing the bias of subjective judgment and improving the credibility of the assessment results.

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] See also Figure 1 , which is a flow chart of a method for risk scoring of vehicle data provided by an embodiment of the present application, such as Figure 1 As shown, the risk scoring method for vehicle data may include steps S101-S105:

[0050] S101: Collect vehicle data of a target vehicle.

[0051] Collecting vehicle data of the target vehicle refers to obtaining various data information automatically collected by a specific vehicle from analog and digital test units such as sensors and other test equipment, such as data on the vehicle's surroundings, vehicle status, and driving behavior. This process is usually achieved through sensors, cameras, radars and other devices carried by the vehicle. These sensors can collect vehicle engine operating data, driving speed, acceleration, fuel consumption, etc. In addition, vehicle-mounted cameras or global positioning system (GPS) devices can also be used to record vehicle surrounding environment information or vehicle location.

[0052] Specifically, the vehicle data of the target vehicle can be collected in the following ways: installing special data collection equipment (such as data loggers, sensors), using the vehicle communication system to transmit data to central storage or cloud databases; connecting through the on-board diagnostic system (OBD) interface to obtain vehicle diagnostic information; using the on-board camera to capture video monitoring data or driver behavior data; using the GPS module to record the vehicle location and trajectory, etc. These methods help collect rich vehicle data and provide a basis for subsequent analysis and evaluation.

[0053] It should be noted that the target vehicle refers to any vehicle whose vehicle data needs to be scored for risk.

[0054] S102: Classify the vehicle data based on vehicle data categories to obtain multiple vehicle data sets, and determine a privacy data set in each of the vehicle data sets.

[0055] After collecting the vehicle data of the target vehicle, the collected vehicle data needs to be classified according to the characteristics of its data type to form multiple different data sets. At the same time, the private data contained in each data set is identified and marked, and each data set is given a clear category label for subsequent processing and analysis.

[0056] The specific operations are as follows:

[0057] (1) Define data categories: First, it is necessary to determine the different categories of vehicle data. These categories may include but are not limited to vehicle exterior data, vehicle cabin data, vehicle operation data, and vehicle location trajectory data.

[0058] It should be noted that off-vehicle data refers to data such as roads, buildings, terrain, and traffic participants collected from the external environment of the car through sensors such as cameras and radars, as well as data generated after processing. Note: Traffic participants refer to people who participate in traffic activities, including drivers and passengers of motor vehicles, non-motor vehicles, and other means of transportation, as well as other people involved in traffic activities. Off-vehicle data may include personal information such as faces and license plates, as well as important data specified by laws, regulations, and standards such as vehicle flow and logistics.

[0059] Vehicle cockpit data refers to data collected from the vehicle cockpit through sensors such as cameras, infrared sensors, fingerprint sensors, microphones, and data generated after processing. Note: Vehicle cockpit data may contain sensitive personal information such as the driver and passengers' faces, voiceprints, fingerprints, heart rates, etc. Cockpit data does not include operation record data generated by processing vehicle collected data.

[0060] Vehicle operation data refers to data collected from electronic and electrical systems such as the power system, chassis system, body system, and comfort system through vehicle speed sensors, temperature sensors, shaft speed sensors, pressure sensors, etc. Note: Operation data includes vehicle control data, operation status data, system operating parameters, and operation record data.

[0061] Vehicle location trajectory data refers to data related to vehicle location and route obtained through various means such as satellite positioning and communication networks. Note: Location trajectory data may contain geographic information of important sensitive areas.

[0062] (2) Classification and division of data sets: Classify the collected vehicle data according to the data categories defined above. This process can be achieved through data cleaning and preprocessing techniques, such as data screening, filtering, grouping, etc., to ensure that each piece of data is correctly assigned to the corresponding category.

[0063] (3) Identify private data: In each data set, further identify the part containing private information. Private data may include personal general information data set, personal important information data set, personal sensitive information data set, and important data information data set.

[0064] Specific methods for identifying privacy data can be formulated based on relevant laws and regulations or company policies to ensure compliance with data protection and privacy protection requirements.

[0065] It should be noted that the data in the personal general information set refers to data that will cause certain harm to users after being leaked, illegally provided or abused, but the scope and degree of the impact are relatively limited and will not pose a threat to property and personal safety.

[0066] The data in the personal important information data set refers to data that, if leaked, provided in violation of regulations or abused, will cause great harm to the user's life and property, and even affect the personal reputation and physical and mental health to a certain extent.

[0067] The data in the personal sensitive information data set refers to personal information that, once leaked or illegally used, may cause discrimination against vehicle owners, drivers, passengers, people outside the vehicle, etc., or cause serious harm to their personal or property safety.

[0068] The data in the important data information data set refers to data that may endanger the public interest or the legitimate rights and interests of individuals and organizations once it is tampered with, destroyed, leaked, or illegally obtained or used. Note: The important data information data set includes geographic information, personnel flow, vehicle flow and other data of important sensitive areas, vehicle flow, logistics and other data reflecting the economic operation. The important data information data set also includes the operation data of the car charging network, such as the video and image data outside the car of facial information, license plate information, etc., the personal information of more than 100,000 personal information subjects, and other data determined by the relevant departments that may endanger the public interest or the legitimate rights and interests of individuals and organizations.

[0069] (4) Labeling category labels: Label each data set with different vehicle data category labels. These vehicle data category labels can clearly identify the content of the data set.

[0070] Vehicle data category labels include but are not limited to external vehicle data labels, vehicle cabin data labels, vehicle operation data labels, and vehicle location trajectory data labels.

[0071] It should be noted that the label can be a text description or a specific code or identifier to facilitate rapid identification and use in subsequent data processing and analysis.

[0072] (5) Record and manage data sets: Record and manage multiple divided data sets and their corresponding privacy data parts to ensure the integrity and traceability of each data set.

[0073] These data sets and their privacy data should be properly preserved for subsequent risk analysis, auditing or other purposes.

[0074] Through the above steps, the collected vehicle data can be effectively classified into multiple different data sets, and the category labels of each data set can be clearly marked. At the same time, the privacy data part can be identified, thereby providing basic support for subsequent risk assessment and data analysis.

[0075] See also Figure 2 , Figure 2 A method flow chart of a method for dividing a vehicle data set provided in an embodiment of the present application. Accordingly, in step S102, the vehicle data is divided into categories based on the vehicle data categories to obtain multiple vehicle data sets, which can be specifically implemented through steps A1-A3:

[0076] A1: Identify the vehicle data collection method and collection environment.

[0077] Since vehicle data categories are determined based on the collection method and collection environment, in order to classify vehicle data, it is first necessary to understand the methods used to collect vehicle data and the location or environment where data collection occurs, and to determine the correlation between these collection methods and environments and vehicle data categories.

[0078] To identify the vehicle data collection method and collection environment, we first need to understand how vehicle data is acquired, such as collecting information such as vehicle performance, location, and driving status through sensors, cameras, GPS devices, and other devices. At the same time, we also need to consider the specific environment in which data collection occurs, which may be different locations such as urban roads, highways, and traffic congestion areas. Different collection methods and environments may lead to data diversity and specificity, which will affect subsequent data processing and analysis.

[0079] To determine the correspondence between vehicle data categories and collection methods and environments, it is necessary to classify the collected vehicle data according to the collection methods and environments in order to better understand the relationship between different data types. For example, location data obtained through GPS positioning may be related to driving speed, route planning, etc., while performance data obtained through on-board sensors may be related to engine operating status, fuel consumption, etc. This correspondence helps to classify and organize vehicle data more accurately, providing a basis for subsequent analysis and application.

[0080] A2: Determine the vehicle data category of the vehicle data based on the correspondence between the vehicle data category, the collection method, and the collection environment.

[0081] Based on the correlation between the type of vehicle data and the collection method and collection environment, determining the classification of vehicle data means classifying different types of vehicle data according to the collection method and environment. In this case, vehicle data can be divided into multiple categories, such as vehicle exterior data, vehicle cabin data, vehicle operation data, and vehicle location trajectory data. The content and characteristics of these categories depend on the data collection method and the environment in which it occurs.

[0082] Specifically, based on the correspondence between vehicle data categories and collection methods and environments, the classification of vehicle data can be determined by identifying the data source and its collection equipment (such as cameras, sensors), data collection location (such as inside the vehicle, outside the vehicle), and data type. For example, the data collected by the vehicle camera may include the scene outside the vehicle, the information obtained by the infrared sensor may be the vehicle cabin data, and the information obtained by the vehicle speed sensor or pressure sensor may be the vehicle operation data. The location trajectory data may be obtained through the satellite positioning system. After confirming the data source, collection method, and the meaning of the data, the vehicle data can be effectively classified, providing a basis for subsequent processing and protection of sensitive information. It should be noted that different categories of vehicle data may have privacy and security considerations, so they need to be treated with caution when processing and storing, especially vehicle cabin data and location trajectory data containing sensitive information.

[0083] A3: Divide the vehicle data with the same vehicle data category into the same vehicle data set.

[0084] Classifying vehicle data with the same vehicle data category into the same vehicle data set means aggregating vehicle data belonging to the same category or type into one data set so that it is easier to manage, analyze and process the data. This classification method can organize data more efficiently and centrally process specific types of data when necessary.

[0085] To group data with the same vehicle data category into the same dataset, you first need to clarify the differences and commonalities between different data categories. Classify the data according to the type of vehicle data, and then integrate the data of the same category into one dataset. This usually involves a process of data labeling, filtering, and classification to ensure that each dataset contains information of the same category. For example, based on classification criteria such as vehicle cabin data, off-vehicle data, vehicle operation data, and vehicle location trajectory data, each piece of data can be associated with its corresponding category and classified into the corresponding dataset.

[0086] This classification and organization method helps improve the efficiency of data management, making it more systematic and controllable when processing large amounts of vehicle data. In addition, by grouping similar types of data together, it is easier to further analyze, mine and apply this type of data.

[0087] S103: Based on the correspondence between the data life cycle of the target vehicle and the vehicle data category label annotated by the vehicle data set, the vehicle data set corresponding to the data life cycle of the target vehicle is determined as the target data set.

[0088] After multiple vehicle data sets are obtained, it is necessary to match the different stages and processing processes that the target vehicle data has gone through throughout its life cycle with the different vehicle data category labels annotated in the vehicle data set, thereby confirming the specific vehicle data set corresponding to each data processing link as the target data set. The life cycle of the target vehicle data includes multiple data processing links. That is, by matching the data type and processing method involved in each processing stage of the target vehicle data with the pre-set vehicle data category labels, the vehicle data set used in each stage is identified and confirmed as the target data set.

[0089] In a possible implementation, the data processing link includes any one of a data collection link, a data transmission link, a data storage link, a data use link, a data sharing link, and a data destruction link.

[0090] In a possible implementation, the data collection link in the data life cycle corresponds to an off-vehicle data tag, a vehicle cabin data tag, a vehicle operation data tag, and a vehicle location trajectory data tag;

[0091] The data transmission link in the data life cycle corresponds to the vehicle cockpit data tag;

[0092] The data storage link in the data life cycle corresponds to the vehicle external data tag, the vehicle cabin data tag, the vehicle operation data tag and the vehicle location trajectory data tag;

[0093] The data usage link in the data life cycle corresponds to the vehicle external data tag, vehicle cabin data tag, vehicle operation data tag and vehicle location trajectory data tag;

[0094] The data sharing link in the data life cycle corresponds to the vehicle external data tag, the vehicle cabin data tag, the vehicle operation data tag and the vehicle location trajectory data tag;

[0095] The data destruction link in the data life cycle and the off-vehicle data tags and vehicle location trajectory data tags.

[0096] By matching the vehicle data sets corresponding to each data processing link with standard data categories, we can have a clearer understanding of the characteristics and uses of vehicle data at different stages, which will help to manage and utilize this data more effectively.

[0097] S104: Based on the target data set corresponding to each data processing link and the privacy data set in the target data set, risk analysis is performed on each data processing link of the target vehicle to obtain a risk value of each data processing link.

[0098] Based on the target data set corresponding to each data processing link and the privacy data set in the target data set, risk analysis is performed on each stage of vehicle data processing to obtain the risk value of each data processing link. This means that potential risks are evaluated at different stages of vehicle data processing according to the target data set involved in each data processing step and the privacy data content that may exist therein. This analysis aims to identify and quantify the degree of risk posed to personal information protection by each data processing link so that corresponding measures can be taken to mitigate or eliminate these risks.

[0099] In this process, we must first clarify the target data set of each data processing link, that is, the data set obtained after processing at this stage, and also identify whether it contains potential privacy data sets, that is, data subsets that may leak personal information or sensitive information. Then, when conducting risk assessments on the target data set and privacy data set corresponding to each data processing step, we need to consider factors such as the possible impact of data processing operations on privacy data, data storage security, and potential risks in the data transmission process. Through such risk analysis, we can determine the degree of risk in each data processing link and provide a reference for formulating risk management strategies and protection measures.

[0100] See also Figure 3 , Figure 3 A method flow chart of a method for obtaining a risk value of a data processing link provided in an embodiment of the present application. Accordingly, step S104 performs risk analysis on each data processing link of the target vehicle based on the target data set corresponding to each data processing link and the privacy data set in the target data set to obtain the risk value of each data processing link. Specifically, steps B1-B3 may be used to implement the following:

[0101] B1: Determine multiple risk events corresponding to the data processing link.

[0102] In order to obtain the risk value of each data processing link, it is first necessary to identify the various potential risk events associated with each data processing link.

[0103] It should be noted that the data used in the data collection link, data transmission link, data storage link, data usage link, data sharing link and data destruction link all come from the vehicle data of the target vehicle.

[0104] In a possible implementation, multiple risk events corresponding to the data collection phase include but are not limited to:

[0105] ① Whether the data collected by the vehicle during the data collection process is authorized by the driver.

[0106] ② Whether the data collected by the vehicle during the data collection process is within the collection scope authorized by the driver.

[0107] ③ Whether the data collected by the vehicle in the data collection link contains any of the following: general personal information data, important personal information data, and sensitive personal information data. This can be summarized as whether the data collected by the vehicle in the data collection link contains personal information.

[0108] ④Whether the data collected by the vehicle during the data collection process contains information in the important data set.

[0109] In this case, the weight configuration table of multiple risk events corresponding to the data collection stage is as follows:

[0110] Table 1 Weight configuration table of risk events in the data collection stage

[0111]

[0112] In a possible implementation, multiple risk events corresponding to the data transmission link include but are not limited to:

[0113] ①Whether the vehicle cockpit data is transmitted in the data transmitted to the outside during the data transmission link.

[0114] ② Whether the data transmitted by the vehicle during the data transmission link contains any of the following information: general personal information data, important personal information data, or sensitive personal information data that has not been anonymized. This can be summarized as whether the data transmitted by the vehicle during the data transmission link contains any of the following information: personal information that has not been anonymized.

[0115] ③Whether there are unencrypted important data or other data in the data transmitted by the vehicle during the data transmission link. Other data refers to all data except important data.

[0116] Table 2 Weight configuration table of risk events in data transmission

[0117]

[0118]

[0119] In a possible implementation, multiple risk events corresponding to the data storage link include but are not limited to:

[0120] ① Whether the vehicle uses encryption algorithm to store data during the data storage process. The stored data includes personal general information data, personal important information data, personal sensitive information data, important data information data and other data in the vehicle data of the target vehicle.

[0121] ② Whether the encryption algorithm used by the vehicle in storing data is safe. The stored data includes personal general information data, personal important information data, personal sensitive information data, important data information data and other data in the vehicle data of the target vehicle.

[0122] Table 3 Weight configuration table of risk events in data storage

[0123]

[0124]

[0125]

[0126] In a possible implementation, multiple risk events corresponding to the data usage phase include but are not limited to:

[0127] ① Whether there is data abuse in the vehicle's use of data. The data used includes personal general information data, personal important information data, personal sensitive information data, important data information data and other data in the vehicle data of the target vehicle.

[0128] Table 4 Weight allocation table of risk events in data use

[0129]

[0130]

[0131] In a possible implementation, multiple risk events corresponding to the data sharing link include but are not limited to:

[0132] ① When vehicles share data in the data sharing link, whether the shared data exceeds the authorized scope. The shared data includes personal general information data, personal important information data, personal sensitive information data, important data information data and other data in the vehicle data of the target vehicle.

[0133] ② When the vehicle shares data in the data sharing link, whether the shared data contains any of the personal general information data, personal important information data, and personal sensitive information data that have not been anonymized can be summarized as whether there is personal information that has not been anonymized in the data shared by the vehicle in the data sharing link.

[0134] Table 5 Weight configuration table of risk events in data sharing

[0135]

[0136]

[0137] In a possible implementation, multiple risk events corresponding to the data destruction phase include but are not limited to:

[0138] ①Whether there is any external vehicle data or location trajectory data that has not been destroyed for more than 14 days during the data destruction phase.

[0139] Table 6 Weight configuration table of risk events in the data destruction link

[0140]

[0141]

[0142] B2: Determine the scoring weight of each of the risk events based on the target data set corresponding to the data processing link and the privacy data set in the target data set.

[0143] Based on the target data set corresponding to the data processing link and the privacy data set in the target data set, the scoring weight of each risk event can be queried from the weight configuration table of the above 6 risk events.

[0144] It should be noted that for non-existent risk events, the scoring weight is 0.

[0145] B3: Based on the experience score of each risk event, a weighted sum is performed on multiple risk events corresponding to the data processing link to obtain the risk value of the data processing link.

[0146] For each specific risk event, based on pre-set experience scores or standards, the scores of different risk events are added together in a certain weighted manner to calculate the comprehensive risk level faced by the entire data processing link.

[0147] In this process, each specific risk event will be assigned an experience score, which can reflect the potential threat level of the risk event to the data processing link or the impact on data security and privacy. These experience scores are usually quantitative indicators based on historical data, industry standards or professional knowledge developed by professionals, security experts or organizations.

[0148] By weighting and summing the experience scores of each risk event, the comprehensive risk level faced by the entire data processing link can be more accurately assessed. This method can better understand the various risks that may exist in the data processing process and take targeted measures to manage, mitigate or eliminate these risks to ensure the security, stability and compliance of data processing operations.

[0149] S105: Performing weighted summation on the risk values ​​of each data processing link based on the preset weights of each data life cycle to obtain a data risk score for the target vehicle.

[0150] Based on the pre-set weight values ​​at different stages of the data life cycle, the risk value of each data processing link is weighted and calculated, and the data risk score of the target vehicle is comprehensively derived.

[0151] In this process, the data lifecycle is divided into different stages or processing links, each of which has its own unique importance and risk level. By setting a predetermined weight for each stage or link, the relative importance of the stage in the entire data processing process can be reflected. The risk value of each data processing link is then weighted according to the weight of the data lifecycle stage to which it belongs, in order to consider the contribution of different stages to the overall data risk.

[0152] The data risk score of the target vehicle is obtained by weighted summing up the risk values ​​of each data processing link. This risk score can more comprehensively reflect the potential risk level faced by the target vehicle throughout the data life cycle, help decision makers better understand the status quo of data security and privacy protection, identify key risk points, and formulate corresponding risk management strategies and measures to ensure the security and reliability of data processing operations.

[0153] Based on the contents of S101-S105, it can be known that the vehicle data of the target vehicle is first collected, and divided according to the categories of these data to form multiple vehicle data sets, each of which contains different privacy data sets and is marked with corresponding data category labels. Next, based on the correlation between the data life cycle of the target vehicle and the category label of the corresponding vehicle data set, the target data set is determined. The data life cycle here includes multiple data processing links. Then, risk analysis is performed on the target data set in these data processing links and the privacy data set therein, so as to obtain the risk value of each link. Finally, the above risk values ​​are weighted and summed using the preset weights of each data life cycle link to obtain the overall data risk score of the target vehicle, thereby achieving a comprehensive assessment of the security status of vehicle data. This application can provide more objective and accurate risk assessment results through data category division and risk analysis of the life cycle stage, and weighted summation to calculate the risk score, which helps to eliminate subjective bias and improve the credibility of the assessment.

[0154] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of a vehicle data risk scoring device provided in an embodiment of the present application. Figure 4 As shown, the risk scoring device for vehicle data includes:

[0155] The vehicle data collection unit 401 is used to collect vehicle data of the target vehicle;

[0156] A first data division unit 402, configured to classify the vehicle data based on vehicle data categories to obtain a plurality of vehicle data sets;

[0157] A privacy data set determining unit 403 is used to determine a privacy data set in each of the vehicle data sets; each vehicle data set is annotated with a different vehicle data category label;

[0158] A target data set determining unit 404 is used to determine the vehicle data set corresponding to the data life cycle of the target vehicle as the target data set based on the correspondence between the data life cycle of the target vehicle and the vehicle data category label annotated by the vehicle data set; the data life cycle includes multiple data processing links;

[0159] A risk analysis unit 405 is used to perform risk analysis on each data processing link of the target vehicle based on the target data set corresponding to each data processing link and the privacy data set in the target data set to obtain a risk value of each data processing link;

[0160] The first calculation unit 406 is used to perform weighted summation on the risk values ​​of each data processing link based on the preset weights of each data life cycle to obtain the data risk score of the target vehicle.

[0161] In a possible implementation, the risk analysis unit 405 specifically includes:

[0162] A risk event determination unit, used to determine a plurality of risk events corresponding to the data processing link;

[0163] A scoring weight determination unit, configured to determine a scoring weight for each of the risk events based on the target data set corresponding to the data processing link and a privacy data set in the target data set;

[0164] The second calculation unit is used to perform weighted summation on multiple risk events corresponding to the data processing link based on the experience score of each risk event to obtain the risk value of the data processing link.

[0165] In a possible implementation, the first data partitioning unit 402 specifically includes:

[0166] A single identification element, used to identify the vehicle data collection method and collection environment; the vehicle data category has a corresponding relationship with the collection method and the collection environment;

[0167] a vehicle data category determination unit, configured to determine the vehicle data category of the vehicle data based on a correspondence between the vehicle data category and the collection method and the collection environment;

[0168] The second data division unit is used to divide the vehicle data with the same vehicle data category into the same vehicle data set.

[0169] In a possible implementation, the vehicle data category labels include an external vehicle data label, a vehicle cabin data label, a vehicle operation data label, and a vehicle location trajectory data label.

[0170] In a possible implementation, the data processing link includes any one of a data collection link, a data transmission link, a data storage link, a data use link, a data sharing link, and a data destruction link.

[0171] In a possible implementation, the privacy data set includes a personal general information data set, a personal important information data set, a personal sensitive information data set, and an important data information data set.

[0172] In addition, an embodiment of the present application also provides a risk scoring device for vehicle data, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the risk scoring method for vehicle data as described above is implemented.

[0173] In addition, an embodiment of the present application further provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a terminal device, the terminal device executes the risk scoring method for vehicle data as described above.

[0174] The risk scoring device for vehicle data provided in the embodiment of the present application provides a more objective and accurate risk assessment result by performing risk analysis based on data classification and life cycle stages and calculating risk scores by weighted summation. This not only helps to eliminate the bias caused by subjective judgment and improve the credibility of the assessment results, but also by conducting detailed risk analysis and calculating risk values ​​for each data processing link, it can better identify and quantify the security risks of each link, thereby timely discovering potential security vulnerabilities and taking corresponding measures. In addition, by setting preset weights for different data processing links and performing weighted summation, a comprehensive risk score can be obtained, making risk assessment more flexible and in line with specific needs. This method not only improves the overall security management level, but also reduces the impact of data security incidents.

[0175] The above is a detailed introduction to a risk scoring method, device, equipment and storage medium for vehicle data provided by the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the embodiments can be referenced to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

[0176] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0177] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

Claims

1. A risk scoring method for vehicle data, characterized in that: The method comprises: Collecting vehicle data of the target vehicle; Classifying the vehicle data based on vehicle data categories to obtain multiple vehicle data sets, and determining a privacy data set in each of the vehicle data sets; each vehicle data set is annotated with a different vehicle data category label; Based on the correspondence between the data life cycle of the target vehicle and the vehicle data category label annotated by the vehicle data set, the vehicle data set corresponding to the data life cycle of the target vehicle is determined as the target data set; the data life cycle includes multiple data processing links; Based on the target data set corresponding to each data processing link and the privacy data set in the target data set, risk analysis is performed on each data processing link of the target vehicle to obtain a risk value of each data processing link; The risk value of each data processing link is weighted and summed based on the preset weight of each data life cycle to obtain the data risk score of the target vehicle.

2. The method according to claim 1, characterized in that The step of performing risk analysis on each data processing link of the target vehicle based on the target data set corresponding to each data processing link and the privacy data set in the target data set to obtain the risk value of each data processing link includes: Determine a plurality of risk events corresponding to the data processing link; Determining a scoring weight for each of the risk events based on the target data set corresponding to the data processing link and a privacy data set in the target data set; Based on the experience score of each risk event, a weighted sum is performed on multiple risk events corresponding to the data processing link to obtain the risk value of the data processing link.

3. The method according to claim 1, characterized in that The step of classifying the vehicle data based on the vehicle data category to obtain a plurality of vehicle data sets includes: Identify the vehicle data collection method and collection environment; the vehicle data category has a corresponding relationship with the collection method and the collection environment; Determining the vehicle data category of the vehicle data based on the correspondence between the vehicle data category and the collection method and the collection environment; The vehicle data having the same vehicle data category are grouped into the same vehicle data set.

4. The method according to claim 1, characterized in that: The vehicle data category labels include vehicle exterior data labels, vehicle cabin data labels, vehicle operation data labels, and vehicle location trajectory data labels.

5. The method according to claim 1, characterized in that The data processing link includes any one of a data collection link, a data transmission link, a data storage link, a data use link, a data sharing link and a data destruction link.

6. The method according to claim 1, characterized in that The privacy data set includes a personal general information data set, a personal important information data set, a personal sensitive information data set and an important data information data set.

7. A risk scoring device for vehicle data, characterized in that: The device comprises: A vehicle data collection unit, used to collect vehicle data of a target vehicle; A first data division unit, configured to classify the vehicle data based on vehicle data categories to obtain a plurality of vehicle data sets; A privacy data set determination unit, used to determine a privacy data set in each of the vehicle data sets; each vehicle data set is annotated with a different vehicle data category label; A target data set determining unit, configured to determine the vehicle data set corresponding to the data life cycle of the target vehicle as the target data set based on the correspondence between the data life cycle of the target vehicle and the vehicle data category label annotated by the vehicle data set; the data life cycle includes a plurality of data processing links; A risk analysis unit, configured to perform risk analysis on each data processing link of the target vehicle based on the target data set corresponding to each data processing link and the privacy data set in the target data set to obtain a risk value of each data processing link; The first calculation unit is used to perform weighted summation on the risk values ​​of each data processing link based on the preset weights of each data life cycle to obtain the data risk score of the target vehicle.

8. The device according to claim 7, characterized in that The risk analysis unit specifically includes: A risk event determination unit, used to determine a plurality of risk events corresponding to the data processing link; A scoring weight determination unit, configured to determine a scoring weight for each of the risk events based on the target data set corresponding to the data processing link and a privacy data set in the target data set; The second calculation unit is used to perform weighted summation on multiple risk events corresponding to the data processing link based on the experience score of each risk event to obtain the risk value of the data processing link.

9. A risk scoring device for vehicle data, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the risk scoring method for vehicle data according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the risk scoring method for vehicle data according to any one of claims 1 to 6.

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