Hierarchical access control and secure circulation method for intelligent transportation data

Abstract

The present invention discloses a hierarchical access control and secure circulation method for intelligent transportation data. The method first acquires traffic data, automatically identifies data sensitivity through machine learning, and then classifies data based on sensitivity. Furthermore, the data at each level is dynamically adjusted according to a preset security policy, and corresponding access rights and access policies are assigned to each data level. When an access request is received, dynamic permissions are generated by collecting user roles, device attributes, and event context. The dynamic permissions are then matched with preset access rights. Upon a successful match, data is released to the user according to the corresponding access policy. This application effectively improves the data security and reliability of intelligent transportation systems and can flexibly address the complexity and diversity of intelligent transportation systems.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more particularly to a hierarchical access control and secure circulation method for intelligent traffic data. Background Art

[0002] Intelligent transportation systems are becoming increasingly important in urban development. By integrating advanced information technology, data communication and transmission technology, electronic sensing technology, control technology, and computer processing technology, they effectively integrate the entire transportation management system, thereby establishing a comprehensive transportation management system that operates on a large scale and in all directions, and is real-time, accurate, and efficient.

[0003] Traffic data is a core resource in intelligent transportation systems, its value lies in providing decision support for traffic management, planning, operations, and public transportation services. However, with the widespread application of intelligent transportation systems and the explosive growth of data volumes, ensuring the security and privacy of traffic data has become a pressing issue. Traffic data often contains a large amount of sensitive information, such as personal information, vehicle location information, and traffic flow information. Once leaked or illegally accessed, it can lead to serious consequences, such as the loss of personal privacy, increased traffic congestion, and increased risk of traffic accidents.

[0004] Existing methods for traffic data security management primarily rely on traditional access control and encryption technologies, which suffer from numerous drawbacks. For example, traditional access control technologies are typically based on static rules and policies, making it difficult to conduct dynamic and accurate sensitivity assessments based on the actual content and context of the data. Furthermore, existing encryption technologies and access control policies are also typically static, making it difficult to dynamically adjust them based on data usage scenarios, user roles, device attributes, and changes in the external environment.

[0005] In addition, the current permission management model is usually based on fixed role-based permissions, which makes it difficult to achieve dynamic permission generation and management based on fine-grained data and specific access contexts. At the same time, due to concerns about data security and privacy, the sharing and circulation of traffic data are often restricted, which hinders the widespread application of intelligent traffic data and the development of value-added services.

[0006] Therefore, how to overcome the above problems is an urgent problem that those skilled in the art need to solve. Summary of the Invention

[0007] In view of this, in order to at least partially solve the above technical problems, the present invention provides a hierarchical access control and secure circulation method for intelligent traffic data, aiming to better protect the security and privacy of intelligent traffic data.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A hierarchical access control and secure circulation method for intelligent traffic data, comprising the following steps:

[0010] Acquire traffic data, combine machine learning to automatically identify data sensitivity, and classify data based on sensitivity;

[0011] Dynamically adjust data at all levels according to preset security policies, and assign corresponding access rights and access policies to each data level;

[0012] When receiving access requests, user roles, device attributes, and event context are collected to generate dynamic permissions;

[0013] Match dynamic permissions with preset access permissions. When a match is successful, data is released to the user according to the corresponding access policy.

[0014] Furthermore, the intelligent traffic data includes at least one type of traffic data selected from video streams, vehicle trajectories, sensor data, and text reports.

[0015] Furthermore, machine learning is combined to automatically identify data sensitivity, including:

[0016] Multimodal feature extraction, used to improve the accuracy and comprehensiveness of data sensitivity assessments, includes: performing target detection on video stream data, identifying license plates and facial regions contained therein, and calculating confidence levels; performing stop-point clustering analysis on vehicle trajectory data, identifying frequently visited areas and their correlation with pre-set sensitive areas; and performing natural language processing on text report data to extract personal identifiers, relevant sensitive keywords, and their corresponding semantic weights.

[0017] The pre-trained machine learning model is used to calculate the comprehensive sensitivity score of the data based on the number and confidence of detected targets, the spatiotemporal correlation strength between trajectory data and preset sensitive areas, and the semantic weight of sensitive keywords in the text.

[0018] This application combines machine learning to automatically identify data sensitivity, and can perform dynamic and accurate sensitivity assessments based on the actual content and context of the data, overcoming the limitations of traditional access control technology based on static rules and policies.

[0019] Furthermore, metadata corresponding to the intelligent transportation data is extracted, and hierarchical decisions are dynamically adjusted based on the metadata; the metadata includes collection time, geographic location, and data source device identifier; the dynamic adjustment includes:

[0020] When the data source is associated with a public safety emergency, the confidentiality level will be increased by one, up to a maximum of confidential level.

[0021] When the geographical location of the data is within the preset range around a school or hospital, the sensitivity score is automatically increased by 0.2-0.5;

[0022] When the timestamp of the data exceeds the preset retention period, automatic degradation processing is performed.

[0023] Dynamic adjustment of grading decisions based on metadata can ensure the real-time and accuracy of grading decisions.

[0024] Furthermore, security policies are used to further protect data security and privacy, including:

[0025] Confidential-level data is encrypted and stored using national secret algorithms;

[0026] Perform field-level dynamic masking on internal-level data;

[0027] For public-level data, regional aggregate statistics are generated and individual identifiers are removed.

[0028] Furthermore, device attributes include device type, network environment, and geographic location; context content includes: emergency response level of requested data, data timeliness, and compliance requirements.

[0029] Furthermore, dynamic permissions are regulated in real time through time window control, behavior pattern triggering, and event-driven adjustment. The goal is to change permission policies in real time based on external events, thereby improving the flexibility and responsiveness of the system.

[0030] The time window control refers to the validity period of the role authority binding.

[0031] The behavior pattern triggering includes real-time analysis of access logs and freezing permissions after identifying abnormal patterns;

[0032] The event-driven adjustment refers to changing the permission policy in real time according to external events.

[0033] Through the above-mentioned dynamic permission generation and management, this application realizes dynamic permission generation and management based on fine-grained data and specific access context, overcoming the defects of the traditional permission management model that allocates permissions based on fixed roles.

[0034] Furthermore, the access policy includes:

[0035] For confidential data, decryption keys corresponding to each role in the access list are pre-configured, and the mapping between roles and keys is stored in a secure key management module. When a user accesses confidential data, after user authority verification is passed, the corresponding decryption key is obtained from the key management module based on the user role and distributed to the user. The decryption key has time limits, number limits, and / or device binding limits.

[0036] For content-level data, dynamic desensitization technology is combined to partially hide or deform sensitive information based on user roles and access context;

[0037] For public-level data, data aggregation and anonymization technologies are used to remove all personally identifiable information and only display statistical information or regional aggregated data.

[0038] Furthermore, the physical network is divided into multiple isolated virtual network slices, each network slice corresponds to different service quality parameters, including but not limited to bandwidth, latency, and reliability; according to the level of traffic data, the traffic data is allocated to the network slice with the corresponding security level for transmission, so as to further enhance the security of data transmission.

[0039] The present invention discloses a hierarchical access control and secure circulation method for intelligent traffic data, which is used to classify traffic data according to factors such as the sensitivity, importance and purpose of the data, and encrypt the access data based on the classification strategy, so as to promote the sharing and circulation of traffic data while ensuring data security and privacy.

[0040] Compared with the existing technology, this invention first realizes the accurate quantitative assessment of data sensitivity by integrating machine learning and multimodal feature analysis technology, then constructs a three-layer dynamic protection system of "grading-adjustment-execution", and finally innovatively proposes a multi-dimensional dynamic permission generation model.

[0041] This application can effectively improve the data security and reliability of intelligent transportation systems by supporting the combined application of multiple security technologies and strategies, and can flexibly respond to the complexity and diversity of intelligent transportation systems, thereby providing strong support for the widespread application of intelligent transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0043] Figure 1 This is a flow chart of the hierarchical access control and secure circulation method for intelligent traffic data of the present invention;

[0044] Figure 2 This is a structural diagram of the hierarchical access control and secure circulation system for intelligent traffic data of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] The following description sets forth many specific details to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0047] The present invention discloses a flexible, efficient and safe traffic data access control and safe circulation method, such as Figure 1 The main steps include:

[0048] Acquire traffic data, combine machine learning to automatically identify data sensitivity, and classify data based on sensitivity;

[0049] Dynamically adjust data at all levels according to preset security policies, and assign corresponding access rights and access policies to each data level;

[0050] When receiving access requests, user roles, device attributes, and event context are collected to generate dynamic permissions;

[0051] Match dynamic permissions with preset access permissions. When a match is successful, data is released to the user according to the corresponding access policy.

[0052] The following describes the implementation process of each step:

[0053] In one embodiment,

[0054] First, traffic data is acquired, and then machine learning is used to automatically identify data sensitivity and classify it according to data sensitivity.

[0055] In this embodiment, the intelligent traffic data includes at least one of video streams, vehicle trajectories, sensor data, and text reports. Preferably, the traffic data also includes traffic accident data, traffic flow, OD, infrastructure information, etc.

[0056] It also combines machine learning to automatically identify data sensitivity, including:

[0057] Multimodal feature extraction:

[0058] Target detection is performed on video stream data to identify the license plate and face areas contained therein, and their clarity index is calculated. In one embodiment, a convolutional neural network model is used to identify the license plate bounding box in the video frame, and the license plate characters are extracted through optical character recognition. A face detection model is used to locate the face area, and its structural similarity index is calculated to evaluate the recognizability.

[0059] Perform stop point clustering analysis on vehicle trajectory data to identify high-frequency visit areas and their correlation with preset sensitive areas; this application identifies vehicle stop points based on a density clustering algorithm and calculates the minimum distance between each stop point and a location in a preset sensitive area library; when there is a stop point with a distance less than a set threshold from a sensitive area, a spatiotemporal correlation strength value is generated based on the minimum distance.

[0060] This embodiment can use one of the following methods or a combination thereof to generate the spatiotemporal correlation strength value:

[0061] Linear function: If the minimum distance is less than the threshold, a linear function can be used to generate the association strength value, for example, association strength value = threshold - minimum distance. In this way, the closer the distance, the higher the association strength value.

[0062] Non-linear function: To describe the association strength more precisely, non-linear functions, such as exponential function or logarithmic function, can be used to generate the association strength value.

[0063] Gradual assignment: Divide the minimum distance into several intervals, each of which corresponds to a fixed association strength value. For example, the association strength value for a distance between 0 and 10 meters is 10, for 10 to 20 meters it is 5, and so on.

[0064] Perform natural language processing on text report data to extract personal identifiers, relevant sensitive keywords, and corresponding semantic weights;

[0065] The pre-trained machine learning model is used to calculate the comprehensive sensitivity score of the data based on the number and confidence of direct identifiers detected, the spatiotemporal correlation strength between trajectory data and sensitive areas, and the semantic weight of sensitive keywords in the text.

[0066] This embodiment trains the machine learning model in the following manner: constructing a multimodal training dataset containing triple samples of video clips, vehicle trajectory sequences, and accident report text; designing a cross-modal attention mechanism to dynamically adjust the contribution weights of different data modalities to the overall sensitivity score; and adopting a weighted cross-entropy loss function to increase the penalty coefficient for misjudgment of confidential data.

[0067] In an exemplary embodiment, when the comprehensive sensitivity score is greater than or equal to a first threshold, the data is marked as confidential; when the score is between a second threshold and the first threshold, it is marked as internal; when the score is lower than the second threshold, it is marked as public.

[0068] To further optimize the above technical solution, metadata corresponding to intelligent transportation data is extracted and hierarchical decisions are dynamically adjusted based on the metadata. The metadata includes the collection time, geographic location, and data source device identifier. Dynamic adjustments include:

[0069] When the data source is associated with a public safety emergency, the confidentiality level will be increased by one, up to a maximum of confidential level.

[0070] When the geographical location of the data is within the preset range around a school or hospital, the sensitivity score is automatically increased by 0.2-0.5;

[0071] When the timestamp of the data exceeds the preset retention period, automatic degradation processing is performed.

[0072] In one embodiment,

[0073] Dynamically adjust data at all levels according to preset security policies, and assign corresponding access rights and access policies to each data level;

[0074] In this embodiment,

[0075] Security policies include:

[0076] Confidential-level data is encrypted and stored using the national secret algorithm. For confidential-level data, this embodiment uses the strongest encryption algorithm, such as the symmetric encryption algorithm (such as SM4) or the asymmetric encryption algorithm (such as SM2) in the national secret algorithm.

[0077] Perform field-level dynamic desensitization on internal-level data; dynamic desensitization includes: displaying the first two digits of the license plate number in plain text and masking the subsequent characters; blurring the timestamp, retaining the date but hiding the specific time and minute information; converting the geographic location information into a regional grid code, hiding the detailed coordinates.

[0078] For public-level data, regional aggregate statistics are generated and individual identifiers are removed.

[0079] Access policies include:

[0080] For confidential data, decryption keys corresponding to each role in the access list are pre-configured, and the mapping between roles and keys is stored in a secure key management module, which is accessible only to authorized administrators. When a user accesses confidential data, the corresponding decryption key is obtained from the key management module based on the user role after user authority verification is passed, and distributed to the user. The decryption key has time limits, number limits, and / or device binding limits.

[0081] Time limit: The key can only be used within a specific time period and will automatically become invalid after the expiration date;

[0082] Limitation of times: The key can only be used a limited number of times and will be automatically destroyed after the limit is reached;

[0083] Device binding: The key can only be used on a specific device bound to the user account;

[0084] Environmental restrictions: The key can only be used in a specific network environment or security environment.

[0085] For content-level data, appropriate encryption technology is used in combination with dynamic desensitization technology to partially hide or deform sensitive information based on user roles and access context;

[0086] For content-level data, a slightly lower encryption strength than that for confidential-level data can be used, but the security of the data still needs to be guaranteed. Symmetric encryption algorithms, such as AES or SM4, can be selected and encrypted using different keys. In addition to encryption, content-level data can also use dynamic desensitization technology. The system desensitizes sensitive information in real time based on the user's role and access request, such as partially masking or replacing the name, ID number, mobile phone number, etc. In this embodiment, the system strictly controls access rights to content-level data, and only authorized users can access it. At the same time, the system will record user access behavior and conduct audits.

[0087] For public-level data, data aggregation and anonymization technologies are used to remove all personally identifiable information and only display statistical information or regional aggregated data.

[0088] For public-level data, this application aggregates individual data into statistical information and removes all personally identifiable information, such as name and ID number. Regional aggregate statistics are then obtained: for example, public-level data may only display traffic flow statistics for a certain area, without showing specific vehicle trajectories or personal information. Furthermore, the system completely removes all individual identifiers to ensure that public data cannot be traced back to specific individuals.

[0089] In this embodiment, even for public-level data, the system monitors access behavior to prevent malicious attacks, such as denial of service attacks (DDoS).

[0090] To further optimize the above technical solution, the physical network is divided into multiple isolated virtual network slices, each network slice corresponding to different service quality parameters, including but not limited to bandwidth, latency, and reliability; according to the level of traffic data, the traffic data is allocated to the network slice with the corresponding security level for transmission.

[0091] The confidential data is assigned to the network slice with the highest security level, which uses end-to-end encryption and uses a key management module to securely distribute and manage encryption keys, ensuring that only authorized users can obtain decryption keys;

[0092] The content-level data is allocated to the network slice with a medium security level, which uses an appropriate encryption algorithm combined with dynamic desensitization technology to protect sensitive information of the data;

[0093] The public-level data is allocated to the network slice with the lowest security level, which uses data aggregation and anonymization technology to completely remove individual identifiers to ensure that no individual identity can be identified;

[0094] The isolation of network slicing prevents interference and unauthorized access between data at different security levels, effectively ensuring the security, reliability, and efficiency of data transmission. Furthermore, the network slicing setup facilitates fine-grained access control within the slice, such as limiting access time and scope.

[0095] In one embodiment,

[0096] When receiving an access request, the user role, device attributes, and event context are collected to generate dynamic permissions. Device attributes include device type (e.g., various terminal devices), network environment (intranet dedicated line / public network), and geographic location (within 1 km of the accident site / remote access). Context includes the emergency response level of the requested data, data timeliness, and compliance requirements (e.g., GDPR cross-border transfer restrictions).

[0097] In a preferred embodiment, dynamic permissions are regulated in real time through time window control, behavior pattern triggering, and event-driven adjustment; wherein,

[0098] The time window control refers to the validity period of the role authority binding.

[0099] The behavior pattern triggering includes real-time analysis of access logs and freezing permissions after identifying abnormal patterns;

[0100] The event-driven adjustment refers to changing the permission policy in real time according to external events.

[0101] In another embodiment, the present application provides a hierarchical access control and secure circulation system for intelligent traffic data, such as Figure 2 , the system has:

[0102] A centralized policy management module: used to define and update security policies, access rights, and access policies, and provides policy conflict detection and policy simulation functions to ensure policy consistency and effectiveness.

[0103] A distributed access control execution module: deployed at the data source or data access interface, used to receive access requests, collect user roles, device attributes and event context, generate dynamic permissions, match them with preset access permissions, and execute access policies.

[0104] A real-time monitoring and auditing module: used to monitor data access behavior, record access logs, perform anomaly detection and early warning, and support the tracing and auditing of data access behavior.

[0105] A secure key management module: used to generate, store, and manage encryption keys, ensure the security of data encryption and decryption processes, and support regular key rotation and destruction.

[0106] This application can significantly enhance data security through the above solution, including:

[0107] Fine-grained access control: Through machine learning, data sensitivity is automatically identified and classified, combined with dynamic adjustment of preset security policies, to achieve fine-grained access control of intelligent transportation data, ensuring that only authorized users can access data of the corresponding level, effectively preventing data leakage and abuse.

[0108] Multi-dimensional security protection: Combining multiple security strategies, such as national secret algorithm encryption, dynamic desensitization, data aggregation and anonymization technology, and network slicing technology, a comprehensive security protection system is built from multiple dimensions such as data storage, processing, and transmission, significantly improving data security.

[0109] Real-time dynamic permission control: Through time window control, behavior pattern triggering and event-driven adjustment, dynamic permissions are controlled in real time, which can respond to various security threats and risks in a timely manner and effectively prevent unauthorized access and malicious attacks.

[0110] Key security management: Decryption keys for confidential data are managed through a secure key management module, with time limits, number limits, and / or device binding limits set, further reducing the risk of key leakage and ensuring the security of confidential data.

[0111] At the same time, it can effectively improve the level of data privacy protection:

[0112] Protection of Personal Identity Information: By performing natural language processing on text report data, extracting and protecting personal identifiers, and anonymizing public-level data, we effectively protect personal privacy and comply with relevant laws and regulations.

[0113] Minimize exposure of sensitive information: Dynamic desensitization technology for content-level data partially hides or deforms sensitive information based on user roles and access context, minimizing exposure of sensitive information and maximizing data privacy while meeting business needs.

[0114] Data aggregation and anonymization: Data aggregation and anonymization technologies are used on public-level data to completely remove individual identifiers, ensuring that personal identities cannot be identified, further improving the level of data privacy protection.

[0115] In addition, data utilization efficiency is improved:

[0116] Accurate data classification: Automatic data sensitivity identification based on machine learning can classify data more accurately, avoiding the errors and inefficiencies of manual classification and improving the accuracy and efficiency of data classification.

[0117] Flexible access strategies: Different access strategies are preset for different levels of data, including decryption key management, dynamic desensitization, and data aggregation anonymization. This not only ensures data security, but also meets the access needs of different users for different levels of data, thereby improving data utilization efficiency.

[0118] Support data sharing and circulation: Through network slicing technology, independent network slices are allocated to data of different security levels, ensuring the bandwidth and isolation of data transmission, supporting the secure sharing and circulation of data, and promoting the widespread application of intelligent transportation data.

[0119] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0120] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A hierarchical access control and secure circulation method for intelligent traffic data, characterized in that: Acquire traffic data and automatically identify data sensitivity. Intelligent traffic data includes at least one type of traffic data from video streams, vehicle trajectories, sensor data, and text reports. Automatically identify data sensitivity, including: Multimodal feature extraction: Performs target detection on video stream data, identifies license plates and facial regions contained therein, and calculates confidence levels; performs stop-point cluster analysis on vehicle trajectory data, identifies frequently visited areas and their association with pre-set sensitive areas; performs natural language processing on text report data, extracting personal identifiers, relevant sensitive keywords, and their corresponding semantic weights; A pre-trained machine learning model is used to calculate the comprehensive sensitivity score of the data based on the number and confidence of detected targets, the spatiotemporal correlation strength between trajectory data and pre-set sensitive areas, and the semantic weight of sensitive keywords in the text. Data is graded based on its comprehensive sensitivity score, including confidential, internal, and public levels; Dynamically adjust data at all levels and enforce pre-set security policies, assigning appropriate access rights and policies to each data level. Dynamic adjustments include extracting metadata corresponding to intelligent transportation data and dynamically adjusting tiering decisions based on the metadata; the metadata includes collection time, geographic location, and data source device identifier. When the data source is associated with a public safety emergency, the confidentiality level will be increased by one, up to a maximum of confidential level. When the geographical location of the data is within the preset range around a school or hospital, the sensitivity score is automatically increased by 0.2-0.5; When the timestamp of the data exceeds the preset retention period, automatic downgrade processing is performed; The preset security policies include: Confidential-level data is encrypted and stored using national secret algorithms; Perform field-level dynamic masking on internal-level data; For public-level data, generate regional aggregate statistics and remove personal identifiers; When receiving an access request, the user role, device attributes, and event context are collected to generate dynamic permissions. Device attributes include device type, network environment, and geographic location. Context includes the emergency response level of the requested data, data timeliness, and compliance requirements. Match dynamic permissions with preset access permissions. When a match is successful, data is released to the user according to the corresponding access policy.

2. The hierarchical access control and secure circulation method according to claim 1, characterized in that: Real-time regulation of dynamic permissions through time window control, behavior pattern triggering, and event-driven adjustment; The time window control refers to binding the validity period for role permissions; The behavior pattern triggering includes real-time analysis of access logs and freezing permissions after identifying abnormal patterns; The event-driven adjustment refers to changing the permission policy in real time according to external events.

3. The hierarchical access control and secure circulation method according to claim 1, characterized in that: Access policies include: For confidential data, decryption keys corresponding to each role in the access list are pre-configured, and the mapping between roles and keys is stored in a secure key management module. When a user accesses confidential data, after user authority verification is passed, the corresponding decryption key is obtained from the key management module based on the user role and distributed to the user. The decryption key has time limits, number limits, and / or device binding limits. For content-level data, combined with a dynamic desensitization algorithm, sensitive information is partially hidden or deformed based on user roles and access context; For public-level data, data aggregation and anonymization algorithms are used to remove all personally identifiable information and only display statistical information or regional aggregated data.

4. The hierarchical access control and secure circulation method according to claim 3, characterized in that: The physical network is divided into multiple isolated virtual network slices, each network slice corresponds to different service quality parameters, including but not limited to bandwidth, latency, and reliability; according to the level of traffic data, the traffic data is allocated to the network slice with the corresponding security level for transmission.

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