Vehicle data privacy processing method and device under parallel tasks

Abstract

The invention discloses a vehicle data privacy processing method and device under parallel tasks. The method comprises the following steps: acquiring to-be-processed vehicle data in a target task of an intelligent agent; performing hierarchical noise processing on the vehicle data to obtain target data; performing encryption processing on the target data according to a scene self-adaptive encryption strategy to obtain encrypted target data; and calculating a task priority corresponding to the target task based on the constructed task priority evaluation model for transmitting and processing the target data. Thus, through dual protection of differential privacy and encryption, data security is remarkably improved, sensitive data of a user is prevented from being leaked or tampered, and balance between security and real-time performance is achieved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a method and device for processing vehicle data privacy under parallel tasks. Background Art

[0002] In smart cockpits, multiple agents (e.g., voice assistants, navigation systems, etc.) often need to work together to meet user needs. Because multiple agents operate simultaneously in smart cockpits, data exchange is more frequent, involving a large amount of user privacy data. Traditional systems have limited data flow, while multi-agent systems require sharing user data to collaboratively complete complex tasks. As more and more use cases require multi-agent collaboration, traditional data privacy protection methods are no longer able to balance data privacy protection with real-time data transmission. Summary of the Invention

[0003] In view of the above problems, the present invention provides a vehicle data privacy processing method and device under parallel tasks.

[0004] According to a first aspect of the present invention, a vehicle data privacy processing method under parallel tasks is provided, comprising:

[0005] Obtain the vehicle data to be processed in the agent's target task;

[0006] Performing hierarchical noise processing on the vehicle data to obtain target data; wherein the hierarchical noise processing is determined by the sensitivity level of the vehicle data, the mission scenario, and the mission accuracy requirement;

[0007] Encrypting the target data according to a scenario-adaptive encryption strategy to obtain encrypted target data;

[0008] Based on the constructed task priority evaluation model, the task priority corresponding to the target task is calculated for the target data transmission and processing; wherein the task priority evaluation model is determined by weight parameters and task parameters.

[0009] Optionally, performing hierarchical noise processing on the vehicle data to obtain target data includes:

[0010] Identify the data type, mission scenario, and mission accuracy requirements corresponding to the vehicle data;

[0011] Determining a sensitivity level corresponding to the vehicle data according to the data type;

[0012] Determining target noise to be added based on the sensitivity level, the mission accuracy requirement, and the mission scenario;

[0013] The target data is obtained according to the target noise and the vehicle data.

[0014] Optionally, determining the target noise to be added according to the sensitivity level, the task accuracy requirement, and the task scenario includes:

[0015] Determining sensitive parameters according to the sensitivity level, the task accuracy requirement, and the task scenario;

[0016] Determining the sensitive noise corresponding to the vehicle data according to the sensitive parameter and a preset first coefficient;

[0017] The target noise to be added is determined according to the sensitive noise and the preset basic noise.

[0018] Optionally, encrypting the target data according to the scenario-adaptive encryption strategy to obtain the encrypted target data includes:

[0019] Identifying a first scene attribute corresponding to the target data;

[0020] If the first scene attribute corresponding to the target data is a high-frequency interaction scene, encrypting the target data using symmetric encryption to obtain encrypted target data;

[0021] If the first scenario attribute corresponding to the target data is a low-frequency critical data scenario, the target data is encrypted using elliptic curve encryption to obtain encrypted target data.

[0022] Optionally, the step of calculating the task priority corresponding to the target task based on the constructed task priority evaluation model for transmitting and processing the target data includes:

[0023] Identifying task parameters and weight parameters corresponding to the target task; the task parameters include task urgency, resource requirements, and delay tolerance; the weight parameters include a first weight, a second weight, and a third weight;

[0024] determining a first impact value according to the task urgency and the first weight;

[0025] determining a second impact value according to the resource demand and the second weight;

[0026] determining a third impact value according to the delay tolerance and the third weight;

[0027] Calculate the task priority corresponding to the target task according to the first impact value, the second impact value, and the third impact value.

[0028] Optionally, before the step of determining a first impact value according to the task urgency and the first weight, the method further includes:

[0029] identifying a second scene attribute corresponding to the target data;

[0030] If the second task scenario is a driving scenario, defining values of the first weight, the second weight, and the third weight, and satisfying that the first weight is greater than the second weight, and the second weight is greater than the third weight;

[0031] If the second task scene attribute is a parking scene, the values of the first weight, the second weight, and the third weight are defined, and the second weight is greater than the first weight, and the first weight is greater than the third weight;

[0032] If the second task scenario attribute is a high-frequency interaction scenario, the values of the first weight, the second weight and the third weight are defined, and the first weight is greater than the third weight, and the third weight is greater than the second weight.

[0033] Optionally, before the step of obtaining the vehicle data to be processed in the target task of the agent, the method further includes:

[0034] Obtain the basic permissions of the agent;

[0035] According to the target task in the agent, dynamically adjust the basic permissions of the agent to determine the access rights of the agent;

[0036] If abnormal access behavior of the agent is detected, the communication with the agent is interrupted and the access rights of the agent are reduced.

[0037] According to a second aspect of the present invention, a vehicle data privacy processing device under parallel tasks is provided, comprising:

[0038] The acquisition module is used to obtain the vehicle data to be processed in the target task of the intelligent agent;

[0039] a noise processing module, configured to perform hierarchical noise processing on the vehicle data to obtain target data; wherein the hierarchical noise processing is determined by the sensitivity level of the vehicle data, the mission scenario, and the mission accuracy requirements;

[0040] An encryption module is used to encrypt the target data according to a scenario-adaptive encryption strategy to obtain encrypted target data;

[0041] The transmission processing module is used to calculate the task priority corresponding to the target task based on the constructed task priority evaluation model for transmitting and processing the target data; wherein the task priority evaluation model is determined by weight parameters and task parameters.

[0042] According to a third aspect of the present invention, a controller is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the vehicle data privacy processing method under the aforementioned parallel tasks.

[0043] According to a fourth aspect of the present invention, a vehicle is provided, comprising a vehicle body and a controller installed in the vehicle body, wherein the controller executes the vehicle data privacy processing method under the aforementioned parallel tasks.

[0044] The above one or more technical solutions in the embodiments of this specification have at least the following technical effects:

[0045] The embodiments of this specification provide a method and device for handling vehicle data privacy in parallel tasks. The method obtains the vehicle data to be processed in the target task of an intelligent agent, performs graded noise processing on the vehicle data to obtain the target data, encrypts the target data according to a scenario-adaptive encryption strategy to obtain encrypted target data, and then calculates the task priority corresponding to the target task based on a constructed task priority evaluation model for transmission and processing of the target data. In this way, through the dual protection of differential privacy and encryption, data security is significantly improved, user sensitive data is prevented from being leaked or tampered with, and a balance is achieved between security and real-time performance.

[0046] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. Throughout the drawings, the same reference figures denote the same components. In the drawings:

[0048] Figure 1 A flowchart of a vehicle data privacy processing method under parallel tasks in an embodiment of the present invention is shown.

[0049] Figure 2 A block diagram of a vehicle data privacy processing device under parallel tasks in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0051] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0052] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not require further definition or explanation in subsequent drawings.

[0053] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0054] The embodiment of the present invention provides a vehicle data privacy processing method under parallel tasks, combining Figure 1 As shown in the flowchart, the vehicle data privacy processing method under the parallel task includes steps 101 to 104:

[0055] Step 101: Obtain vehicle data to be processed in the agent's target task;

[0056] In this embodiment, the agent refers to the navigation system, entertainment system, voice assistant, etc. in the vehicle cabin. The target task can be the task currently being performed by the agent. At the same time, there may be multiple tasks of different agents that need to be executed in parallel. Vehicle data can include vehicle driving status data, user personal information, driving habits data, etc.

[0057] Step 102: performing hierarchical noise processing on the vehicle data to obtain target data; wherein the hierarchical noise processing is determined by the sensitivity level of the vehicle data, the mission scenario, and the mission accuracy requirement;

[0058] In this embodiment, the specific steps of graded noise processing are mainly determined by the sensitivity level of the vehicle data, the mission scenario, and the mission accuracy requirements. Ultimately, the target noise to be added to the vehicle data is obtained. The specific steps may include:

[0059] Identify the data type, mission scenario, and mission accuracy requirements corresponding to the vehicle data;

[0060] Determining a sensitivity level corresponding to the vehicle data according to the data type;

[0061] Determining target noise to be added based on the sensitivity level, the mission accuracy requirement, and the mission scenario;

[0062] The target data is obtained according to the target noise and the vehicle data.

[0063] The data types are pre-defined and classified, for example, the data types may include location data, user play history data, user habit data, etc. Different data types correspond to different noise intensities.

[0064] Specifically, the sensitivity levels corresponding to vehicle data may include:

[0065] For highly sensitive data (e.g., real-time location data, driving behavior data), stronger noise is added.

[0066] For medium-sensitive data (e.g., vehicle status data, user preference data), medium-intensity noise is added.

[0067] For low-sensitivity data (for example, user historical playback records), weak noise is added.

[0068] This embodiment can also adjust the noise intensity according to the mission scenario the vehicle is in. Mission scenario settings include:

[0069] In urban scenarios, the corresponding location data privacy requirements are high, and stronger noise is added.

[0070] In highway scenarios, higher navigation accuracy is required and noise intensity should be appropriately reduced.

[0071] This embodiment can also adjust the noise intensity according to the target task's requirements for data accuracy, that is, adjust the noise intensity according to the task accuracy requirements. The task accuracy requirements are set as follows:

[0072] The target task is navigation path planning, which requires higher precision and less noise.

[0073] The target task is user behavior analysis, which requires low precision and has high noise.

[0074] This embodiment combines the sensitivity level, mission scenario, and mission accuracy requirements to ultimately determine the target noise level to be added. Specific steps may include:

[0075] Determining sensitive parameters according to the sensitivity level, the task accuracy requirement, and the task scenario;

[0076] Determining the sensitive noise corresponding to the vehicle data according to the sensitive parameter and a preset first coefficient;

[0077] The target noise to be added is determined according to the sensitive noise and the preset basic noise.

[0078] In other words, when adding noise to vehicle data, this embodiment first sets a baseline value, namely the base noise, which represents the minimum noise protection level. It then adjusts the noise based on specific circumstances to obtain a sensitive noise level. Finally, based on the base noise and sensitive noise, the target noise level to be added is determined. By dynamically adjusting the sensitive noise level of vehicle data, this embodiment can better protect user privacy data.

[0079] In specific implementation, the above content can be achieved through a dynamic sensitivity scoring mechanism:

[0080] Based on the data type and task characteristics of vehicle data in the intelligent cockpit, the sensitive parameter S is constructed:

[0081] The value range of S is 0-1. The closer it is to 0, the greater the corresponding sensitive noise.

[0082] The specific value of S is adjusted by the sensitivity level, mission scenario, and mission accuracy requirements.

[0083] The intensity of the target noise is then dynamically adjusted according to the sensitive parameters:

[0084] e=a(1-S)+b

[0085] Among them, e is the target noise; a is the preset first coefficient, which is used to control the impact of sensitive parameters on noise intensity; b is the basic noise, which is the minimum protection level of noise.

[0086] For example:

[0087] In the navigation system, the sensitive parameters of vehicle data are scored as follows:

[0088] In an urban area, user location data is highly sensitive, requiring high precision. A sensitivity parameter score of 0.9 is recommended. Target noise within ±50 meters can be added to prevent precise positioning.

[0089] In the current highway scenario, user location data is highly sensitive, and the task accuracy requirement is not high, with a sensitivity parameter score of 0.6. You can add target noise of ±10 meters to meet the navigation accuracy requirement.

[0090] Step 103: Encrypting the target data according to the scenario-adaptive encryption strategy to obtain encrypted target data;

[0091] In this embodiment, there are many different data interaction scenarios in the smart cockpit, and different data interaction scenarios have different requirements for data security and transmission efficiency. The scenario-adaptive encryption strategy can select the appropriate encryption method according to the specific data interaction scenario, so that the encryption processing can better adapt to the diverse scenario requirements. Whether it is a high-frequency interaction scenario or a low-frequency critical data scenario, a matching encryption method can be found, ensuring the secure transmission and effective processing of data in different data interaction scenarios, and improving the stability and reliability of the entire smart cockpit system. The specific steps of encryption may include:

[0092] Identifying a first scene attribute corresponding to the target data;

[0093] If the first scene attribute corresponding to the target data is a high-frequency interaction scene, encrypting the target data using symmetric encryption to obtain encrypted target data;

[0094] If the first scenario attribute corresponding to the target data is a low-frequency critical data scenario, the target data is encrypted using elliptic curve encryption to obtain encrypted target data.

[0095] Among them, there are at least two first scenario attributes, namely high-frequency interaction scenarios and low-frequency critical data scenarios. In high-frequency interaction scenarios, symmetric encryption is used to encrypt the target data. The symmetric encryption algorithm has a fast encryption and decryption speed and is suitable for scenarios with frequent data transmission. In high-frequency interaction scenarios, data needs to be quickly encrypted and transmitted. Symmetric encryption can meet this requirement, improve the efficiency of data encryption, ensure that data can be transmitted and processed in a timely manner, reduce system latency, and enhance user experience. For example, as a high-frequency interaction scenario, the navigation system updates the path in real time. The target data can be encrypted using AES-128, achieving millisecond-level encryption and decryption to ensure low latency.

[0096] In low-frequency, critical data scenarios, elliptic curve cryptography can be used to encrypt the target data. Elliptic curve cryptography offers high security and is suitable for low-frequency, critical data scenarios with high security requirements. For example, in a low-frequency, critical data scenario where a user completes a payment in a car, using elliptic curve encryption can effectively prevent data theft or tampering, protecting the security and integrity of the data.

[0097] Step 104: Based on the constructed task priority evaluation model, calculate the task priority corresponding to the target task for the target data transmission and processing; wherein the task priority evaluation model is determined by weight parameters and task parameters.

[0098] In this embodiment, the target task refers to the task that the agent currently needs to perform. During the target task, data exchange with other agents may be necessary. Simultaneously, other agents may also have tasks to perform. Therefore, parallel task processing may occur. In this case, the task priority assessment model is used to prioritize the target tasks, prioritizing critical tasks and delaying non-critical tasks.

[0099] The task priority evaluation model of this embodiment dynamically evaluates the execution priority of each target task using multi-dimensional parameters. The specific steps may include:

[0100] Identify task parameters and weight parameters corresponding to the target task;

[0101] determining a first impact value according to the task urgency and the first weight;

[0102] determining a second impact value according to the resource demand and the second weight;

[0103] determining a third impact value according to the delay tolerance and the third weight;

[0104] Calculate the task priority corresponding to the target task according to the first impact value, the second impact value, and the third impact value.

[0105] In this embodiment, task parameters include task urgency, resource requirements, and delay tolerance; the weight parameters include a first weight, a second weight, and a third weight. The weight parameters are dynamically adjusted based on the second scenario parameter. Task priority determines the priority of target data transmission and processing in the case of parallel tasks.

[0106] Among them, task urgency refers to the degree to which the target task needs to be processed immediately, which can also be understood as the time sensitivity of the target task. The higher the value, the more urgent it is. In this embodiment, the urgency corresponding to the task is predefined according to each task, and the corresponding value is defined. For example, the navigation path update task has a corresponding task urgency of 10 points; the intelligent driving mode switching task has a corresponding task urgency of 7 points; and the entertainment task has a corresponding task urgency of 3 points. The product of the task urgency and the first weight can be used to calculate the first impact value.

[0107] Resource requirements refer to the computing resources required to complete the target task, including CPU, memory, and network bandwidth. Higher values indicate greater requirements. Resource requirements can be set based on real-time monitoring of resource consumption in the vehicle. For example, the resource requirement for the intelligent driving system perception task is 8 points; the resource requirement for the navigation path calculation task is 5 points; and the resource requirement for the voice assistant data parsing task is 2 points. The second impact value can be calculated by multiplying the resource requirement by the second weight.

[0108] Delay tolerance refers to the delay time that the target task can tolerate, with higher values indicating longer delays. This embodiment sets a delay threshold based on the type of target task and performs reverse scoring. For example, a navigation path update task has a delay tolerance of 1 point; an entertainment task has a delay tolerance of 5 points; and other non-critical tasks have a delay tolerance of 8 points. The third impact value can be calculated by multiplying the delay tolerance by the third weight.

[0109] In this embodiment, the target sum is calculated based on the first and second impact values, and the difference between the target sum and the third impact value is calculated to finally determine the task priority corresponding to the target task. The task priority determines the priority of target data transmission and processing in the case of parallel tasks.

[0110] In this embodiment, the selection of the weight parameter needs to be adjusted according to the second scene attribute. Specifically, the step of dynamically adjusting the weight parameter according to the second scene attribute of the target data may include:

[0111] identifying a second scene attribute corresponding to the target data;

[0112] If the second task scenario is a driving scenario, defining values of the first weight, the second weight, and the third weight, and satisfying that the first weight is greater than the second weight, and the second weight is greater than the third weight;

[0113] If the second task scene attribute is a parking scene, the values of the first weight, the second weight, and the third weight are defined, and the second weight is greater than the first weight, and the first weight is greater than the third weight;

[0114] If the second task scenario attribute is a high-frequency interaction scenario, the values of the first weight, the second weight and the third weight are defined, and the first weight is greater than the third weight, and the third weight is greater than the second weight.

[0115] Among them, the second scene attributes include at least driving scenes, parking scenes, and high-frequency interaction scenes. In specific use, they can also be added according to actual driving conditions. It should be noted that each weight has corresponding task parameters. Task urgency corresponds to the first weight, resource requirements correspond to the second weight, and delay tolerance corresponds to the third weight. When the second scene attributes are different, the corresponding values of the first weight, second weight, and third weight are different, and the final calculated task priority is also different.

[0116] For example, the second task scenario attribute is a driving scenario:

[0117] The first weight is 0.5, the second weight is 0.3, and the third weight is 0.2. The first weight corresponding to the urgency of the task is higher, ensuring that driving-related tasks are executed first.

[0118] The second task scene attribute is a parking scene:

[0119] The first weight is 0.3, the second weight is 0.5, and the third weight is 0.2. The second weight corresponding to resource demand is higher, which optimizes system resource allocation.

[0120] The second task scenario is a high-frequency interaction scenario (with a high delay tolerance weight):

[0121] The first weight is 0.4, the second weight is 0.2, and the third weight is 0.4. The third weight corresponding to the delay tolerance is high to avoid processing delays for high-frequency tasks.

[0122] To facilitate understanding and implementation by those skilled in the art, this embodiment provides an example of calculating task priority:

[0123] The second task scenario attribute is a driving scenario, specifically task scheduling in a high-speed driving scenario;

[0124] The target task is the navigation path update task:

[0125] The task urgency is 10, the resource requirement is 6, and the delay tolerance is 2.

[0126] The first weight is 0.5, the second weight is 0.3, and the third weight is 0.2.

[0127] Task priority = 0.5*10 + 0.3*6 - 0.2*2 = 6.4

[0128] The target task is the music switching task of the entertainment system:

[0129] The task urgency is 3, the resource requirement is 2, and the delay tolerance is 7.

[0130] Task priority = 0.5*3 + 0.3*2 - 0.2*7 = 0.7

[0131] Conclusion: In the driving scenario, the task priority of the navigation path update task is significantly higher than the music switching task.

[0132] It should be noted that, before the step of obtaining the vehicle data to be processed in the target task of the agent, the method further includes:

[0133] Obtain the basic permissions of the agent;

[0134] According to the target task in the agent, dynamically adjust the basic permissions of the agent to determine the access rights of the agent;

[0135] If abnormal access behavior of the agent is detected, the communication with the agent is interrupted and the access rights of the agent are reduced.

[0136] In this embodiment, role-based access control can limit the data access rights of each agent, thereby ensuring the principle of least privilege.

[0137] In this embodiment, each agent can be pre-assigned corresponding basic permissions. For example, the voice assistant has basic permissions to access user voice commands and some task-related data (such as target location and playback request); the navigation system has basic permissions to access the user's current location, target location, real-time traffic conditions, etc.; the entertainment system has basic permissions to access audio and display control; and the vehicle control system has basic permissions to access core driving data (such as speed, throttle, and brake status).

[0138] This embodiment will perform dynamic permission adjustments on the basic permissions of the agent according to the target tasks in the agent to determine the access rights of the agent.

[0139] Specifically, when an agent needs to temporarily access data beyond its base permissions, it must verify the legitimacy of the target task (for example, a navigation task requires reading fuel data to determine whether the target can be reached). If the target task is verified to be legal, the permissions are temporarily granted. This means dynamically adjusting permissions based on the base permissions to determine the agent's access rights. The temporarily granted permissions are then immediately revoked upon completion of the target task.

[0140] It should be noted that if an agent exhibits abnormal access behavior, the communication with the agent will be interrupted and the access rights of the agent will be reduced.

[0141] Abnormal access behaviors include:

[0142] Frequent requests: An agent requests non-related data multiple times in a short period of time. For example, an entertainment system requests the vehicle's speed multiple times in a short period of time.

[0143] Unauthorized request: An agent attempts to access unauthorized data. For example, a navigation system attempts to obtain the specific content of a user's voice command.

[0144] Abnormal traffic: A surge in data transmission volume may indicate a man-in-the-middle attack or malware stealing data.

[0145] For example:

[0146] The entertainment system attempted to access navigation data multiple times. The vehicle's traffic monitoring module detected an abnormal request frequency and restricted access to the entertainment system, triggering a security alarm and immediately interrupting the anomalous agent's communication. The anomalous agent's access rights were then reduced to only allow access to data within a basic permission range.

[0147] In summary, the embodiments of this specification provide a method and device for handling vehicle data privacy under parallel tasks. The method and device obtain the vehicle data to be processed in the target task of an intelligent agent, perform graded noise processing on the vehicle data, and obtain the target data. The method and device encrypt the target data according to a scenario-adaptive encryption strategy to obtain encrypted target data. The method then calculates the task priority corresponding to the target task based on a constructed task priority evaluation model, which is then used to transmit and process the target data. In this way, through the dual protection of differential privacy and encryption, data security is significantly improved, user sensitive data is prevented from being leaked or tampered with, and a balance is achieved between security and real-time performance.

[0148] Based on the same inventive concept, combined Figure 2 As shown, an embodiment of the present invention further provides a vehicle data privacy processing device under parallel tasks, comprising:

[0149] The acquisition module is used to obtain the vehicle data to be processed in the target task of the intelligent agent;

[0150] a noise processing module, configured to perform hierarchical noise processing on the vehicle data to obtain target data; wherein the hierarchical noise processing is determined by the sensitivity level of the vehicle data, the mission scenario, and the mission accuracy requirements;

[0151] An encryption module is used to encrypt the target data according to a scenario-adaptive encryption strategy to obtain encrypted target data;

[0152] The transmission processing module is used to calculate the task priority corresponding to the target task based on the constructed task priority evaluation model for transmitting and processing the target data; wherein the task priority evaluation model is determined by weight parameters and task parameters.

[0153] Optionally, performing hierarchical noise processing on the vehicle data to obtain target data includes:

[0154] Identify the data type, mission scenario, and mission accuracy requirements corresponding to the vehicle data;

[0155] Determining a sensitivity level corresponding to the vehicle data according to the data type;

[0156] Determining target noise to be added based on the sensitivity level, the mission accuracy requirement, and the mission scenario;

[0157] The target data is obtained according to the target noise and the vehicle data.

[0158] Optionally, determining the target noise to be added according to the sensitivity level, the task accuracy requirement, and the task scenario includes:

[0159] Determining sensitive parameters according to the sensitivity level, the task accuracy requirement, and the task scenario;

[0160] Determining the sensitive noise corresponding to the vehicle data according to the sensitive parameter and a preset first coefficient;

[0161] The target noise to be added is determined according to the sensitive noise and the preset basic noise.

[0162] Optionally, encrypting the target data according to the scenario-adaptive encryption strategy to obtain the encrypted target data includes:

[0163] Identifying a first scene attribute corresponding to the target data;

[0164] If the first scene attribute corresponding to the target data is a high-frequency interaction scene, encrypting the target data using symmetric encryption to obtain encrypted target data;

[0165] If the first scenario attribute corresponding to the target data is a low-frequency critical data scenario, the target data is encrypted using elliptic curve encryption to obtain encrypted target data.

[0166] Optionally, the step of calculating the task priority corresponding to the target task based on the constructed task priority evaluation model for transmitting and processing the target data includes:

[0167] Identifying task parameters and weight parameters corresponding to the target task; the task parameters include task urgency, resource requirements, and delay tolerance; the weight parameters include a first weight, a second weight, and a third weight;

[0168] determining a first impact value according to the task urgency and the first weight;

[0169] determining a second impact value according to the resource demand and the second weight;

[0170] determining a third impact value according to the delay tolerance and the third weight;

[0171] Calculate the task priority corresponding to the target task according to the first impact value, the second impact value, and the third impact value.

[0172] Optionally, the weight parameter is dynamically adjusted according to the second scene attribute of the target data, including:

[0173] identifying a second scene attribute corresponding to the target data;

[0174] If the second task scenario is a driving scenario, defining values of the first weight, the second weight, and the third weight, and satisfying that the first weight is greater than the second weight, and the second weight is greater than the third weight;

[0175] If the second task scene attribute is a parking scene, the values of the first weight, the second weight, and the third weight are defined, and the second weight is greater than the first weight, and the first weight is greater than the third weight;

[0176] If the second task scenario attribute is a high-frequency interaction scenario, the values of the first weight, the second weight and the third weight are defined, and the first weight is greater than the third weight, and the third weight is greater than the second weight.

[0177] Optionally, before the step of obtaining the vehicle data to be processed in the target task of the agent, the method further includes:

[0178] Determining a role corresponding to the agent according to a target task in the agent;

[0179] Determine the access rights of the agent according to the role corresponding to the agent;

[0180] If abnormal access behavior of the agent is detected, the communication with the agent is interrupted and the access rights of the agent are reduced.

[0181] In summary, the embodiments of this specification provide a method and device for handling vehicle data privacy under parallel tasks. The method and device obtain the vehicle data to be processed in the target task of an intelligent agent, perform graded noise processing on the vehicle data, and obtain the target data. The method and device encrypt the target data according to a scenario-adaptive encryption strategy to obtain encrypted target data. The method then calculates the task priority corresponding to the target task based on a constructed task priority evaluation model, which is then used to transmit and process the target data. In this way, through the dual protection of differential privacy and encryption, data security is significantly improved, user sensitive data is prevented from being leaked or tampered with, and a balance is achieved between security and real-time performance.

[0182] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the vehicle data privacy processing device under the parallel tasks described above can refer to the corresponding process in the aforementioned method and will not be elaborated here.

[0183] According to the third aspect of the present invention, a controller is provided, which includes a vehicle data privacy processing device under parallel tasks, a memory, a processor and a communication unit. The memory stores machine-readable instructions executable by the processor. When the controller is running, the processor and the memory communicate through a bus, the processor executes the machine-readable instructions, and executes the vehicle data privacy processing method under parallel tasks.

[0184] The memory, processor, and communication unit components are electrically connected to each other directly or indirectly to achieve signal transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The vehicle data privacy processing device under parallel tasks includes at least one software function module that can be stored in the memory in the form of software or firmware. The processor is used to execute the executable module stored in the memory (for example, the software function module or computer program included in the vehicle data privacy processing device under parallel tasks).

[0185] Among them, the memory can be, but is not limited to, random access memory (Random Access Memory, RAM), read-only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0186] In some embodiments, the processor is used to perform one or more functions described in this embodiment. In some embodiments, the processor may include one or more processing cores (eg, a single-core processor (S) or a multi-core processor (S)).

[0187] In this embodiment, the memory is used to store the program, and the processor is used to execute the program after receiving the execution instruction. The process definition method disclosed in any implementation of this embodiment can be applied to the processor or implemented by the processor.

[0188] The communication unit is used to establish a communication connection between the controller and other devices through the network, and to send and receive data through the network.

[0189] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the controller described above can refer to the corresponding process in the aforementioned method, and will not be elaborated here.

[0190] According to a fourth aspect of the present invention, a vehicle is provided, comprising a vehicle body and a controller installed in the vehicle body, wherein the controller is configured to implement the aforementioned vehicle data privacy processing method under parallel tasks.

[0191] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the vehicle controller described above can refer to the corresponding process in the aforementioned method and will not be elaborated here.

[0192] The above are merely various embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A vehicle data privacy processing method under parallel tasks, characterized in that: include: Obtain the vehicle data to be processed in the agent's target task; Performing hierarchical noise processing on the vehicle data to obtain target data; wherein the hierarchical noise processing is determined by the sensitivity level of the vehicle data, the mission scenario, and the mission accuracy requirement; Encrypting the target data according to a scenario-adaptive encryption strategy to obtain encrypted target data; Based on the constructed task priority evaluation model, the task priority corresponding to the target task is calculated for the target data transmission and processing; wherein the task priority evaluation model is determined by weight parameters and task parameters.

2. The method according to claim 1, characterized in that The step of performing hierarchical noise processing on the vehicle data to obtain target data includes: Identify the data type, mission scenario, and mission accuracy requirements corresponding to the vehicle data; Determining a sensitivity level corresponding to the vehicle data according to the data type; Determining target noise to be added based on the sensitivity level, the mission accuracy requirement, and the mission scenario; The target data is obtained according to the target noise and the vehicle data.

3. The method according to claim 2, characterized in that The determining of the target noise to be added according to the sensitivity level, the task accuracy requirement, and the task scenario includes: Determining sensitive parameters according to the sensitivity level, the task accuracy requirement, and the task scenario; Determining the sensitive noise corresponding to the vehicle data according to the sensitive parameter and a preset first coefficient; The target noise to be added is determined according to the sensitive noise and the preset basic noise.

4. The method according to claim 1, wherein The step of encrypting the target data according to the scenario-adaptive encryption strategy to obtain the encrypted target data includes: Identifying a first scene attribute corresponding to the target data; If the first scene attribute corresponding to the target data is a high-frequency interaction scene, encrypting the target data using symmetric encryption to obtain encrypted target data; If the first scenario attribute corresponding to the target data is a low-frequency critical data scenario, the target data is encrypted using elliptic curve encryption to obtain encrypted target data.

5. The method according to claim 1, characterized in that The task priority corresponding to the target task is calculated based on the constructed task priority evaluation model for transmitting and processing the target data, including: Identifying task parameters and weight parameters corresponding to the target task; the task parameters include task urgency, resource requirements, and delay tolerance; the weight parameters include a first weight, a second weight, and a third weight; determining a first impact value according to the task urgency and the first weight; determining a second impact value according to the resource demand and the second weight; determining a third impact value according to the delay tolerance and the third weight; Calculate the task priority corresponding to the target task according to the first impact value, the second impact value, and the third impact value.

6. The method according to claim 5, characterized in that Before the step of determining a first impact value according to the task urgency and the first weight, the method further includes: identifying a second scene attribute corresponding to the target data; If the second task scenario is a driving scenario, defining values of the first weight, the second weight, and the third weight, and satisfying that the first weight is greater than the second weight, and the second weight is greater than the third weight; If the second task scene attribute is a parking scene, the values of the first weight, the second weight, and the third weight are defined, and the second weight is greater than the first weight, and the first weight is greater than the third weight; If the second task scenario attribute is a high-frequency interaction scenario, the values of the first weight, the second weight and the third weight are defined, and the first weight is greater than the third weight, and the third weight is greater than the second weight.

7. The method according to claim 1, characterized in that Before the step of obtaining vehicle data to be processed in the intelligent agent target task, the method further includes: Obtain the basic permissions of the agent; According to the target task in the agent, dynamically adjust the basic permissions of the agent to determine the access rights of the agent; If abnormal access behavior of the agent is detected, the communication with the agent is interrupted and the access rights of the agent are reduced.

8. A vehicle data privacy processing device under parallel tasks, characterized in that: include: The acquisition module is used to obtain the vehicle data to be processed in the target task of the intelligent agent; a noise processing module, configured to perform hierarchical noise processing on the vehicle data to obtain target data; wherein the hierarchical noise processing is determined by the sensitivity level of the vehicle data, the mission scenario, and the mission accuracy requirements; An encryption module is used to encrypt the target data according to a scenario-adaptive encryption strategy to obtain encrypted target data; The transmission processing module is used to calculate the task priority corresponding to the target task based on the constructed task priority evaluation model for transmitting and processing the target data; wherein the task priority evaluation model is determined by weight parameters and task parameters.

9. A controller, characterized in that: The controller includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the vehicle data privacy processing method under parallel tasks described in any one of claims 1 to 7 is implemented.

10. A vehicle, characterized in that: The vehicle includes a vehicle body and a controller installed in the vehicle body, wherein the controller executes the vehicle data privacy processing method under parallel tasks described in any one of claims 1-7.

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