WO2022226689A1

WO2022226689A1 - Data management method and apparatus, and terminal device

Info

Publication number: WO2022226689A1
Application number: PCT/CN2021/089619
Authority: WO
Inventors: 罗达新; 高鲁涛; 马莎
Original assignee: 华为技术有限公司
Priority date: 2021-04-25
Filing date: 2021-04-25
Publication date: 2022-11-03
Also published as: CN113302614A; CN113302614B

Abstract

Provided are a data management method and apparatus, and a terminal device, which are capable of effectively managing data and preventing the loss of data stored in a volatile memory, and which are especially applicable to a scenario in which real-time requirements for data management are relatively high. The present invention may also extend the service life of a volatile memory. The data management method comprises: first, contained target data which is related to an event that may occur in a vehicle is controlled to be stored in a volatile memory, and then the target data related to the event that may occur in the vehicle is directly or indirectly controlled to be stored in a non-volatile memory.

Description

Data management method, apparatus and terminal device

technical field

The present application relates to the field of storage technology, and more particularly, to a data management method, apparatus and terminal device in the field of storage technology.

Background technique

With the increasing complexity of traffic conditions, the probability of accidents of intelligent transportation devices (such as vehicles) is also increasing. The driving data of the vehicle within a certain time interval before and after the accident, such as sensor data, vehicle state data and vehicle dynamics data, etc., play a vital role in the analysis of the cause of the accident, the restoration of the accident scene and the assessment of the responsibility of the accident.

However, in the event of the above-mentioned event in the vehicle, there may be a situation in which data (such as the driving data of the vehicle) is lost. The loss of data will be detrimental to subsequent accident cause analysis, accident scene restoration and accident liability assessment. Therefore, there is an urgent need to provide a technical solution capable of effectively managing data.

SUMMARY OF THE INVENTION

The embodiments of the present application provide a data management method and device, which can effectively manage data and avoid data loss due to events such as vehicle accidents, vehicle failures, or takeover accidents as much as possible.

In a first aspect, an embodiment of the present invention provides a data management method, including: controlling first reference data to be stored in a volatile memory, where the first reference data includes target data related to a first event; The target data of the memory related to the first event is stored to the non-volatile memory.

Compared with the technical solution of realizing data management through accident data recorder and the technical solution of realizing driving data management through continuous storage and continuous erasing, the data management method provided by the embodiment of the present application can effectively manage data, and can Avoid data loss to the greatest extent possible.

In a possible implementation manner, controlling to store the target data related to the first event from the volatile memory to the non-volatile memory includes: sending indication information to the volatile memory, where the indication information is used to instruct The target data related to the first event is transmitted to the non-volatile memory; or, the target data related to the first event is obtained from the volatile memory, and the target data related to the first event is sent to the non-volatile memory in memory.

The above control stores the target data related to the first event from the volatile memory to the non-volatile memory in two ways, which can manage the driving data of any scale (that is, there is no limit to the data amount of the driving data), And there is no requirement for real-time storage of driving data (that is, it can be applied not only to scenarios that do not require high real-time storage requirements, but also to scenarios that require high real-time storage requirements). In addition, the embodiments of the present application do not need to perform multiple erasing of the non-volatile memory, etc., and can prolong the operating life of the non-volatile memory.

In a possible implementation manner, the first reference data includes at least one of perception data, positioning data, map data, and wireless communication technology data.

In the embodiments of the present application, data such as perception data, positioning data, map data, and wireless communication technology data can be stored in a volatile memory, which can improve the reliability of driving data management.

For example, the perception data may be acquired by one or more perception devices such as a camera, a weather radar, a radar, an ultrasonic radar, or a lidar installed on the vehicle. The positioning data can be obtained by positioning the vehicle through the global positioning system installed on the vehicle. The map data can be data such as road structures, signs, signal lights, buildings and green belts captured by the camera. The wireless communication technology data may be data transmitted between a vehicle (eg, vehicle A) and another vehicle (eg, vehicle B), vehicle A and roadside facilities, vehicle A and communication satellites, etc., obtained through a communication module installed on the vehicle.

It can be seen from the above description that the acquisition methods of sensing data, positioning data, map data, wireless communication technology data, etc. in the first reference data RD1 are relatively simple, thereby making the data management method provided by the embodiment of the present application easy to implement.

In a possible implementation manner, the data management method provided in the first aspect further includes: obtaining a first reference result through the event prediction model and the first reference data, where the first reference result is used to indicate that the first reference data is obtained based on the first reference data. the probability of occurrence of the first event.

In a possible implementation manner, the data management method provided by the first aspect further includes: obtaining a second reference result by using the first reference data and the auxiliary data, where the second reference result is used to indicate that the first reference data and the auxiliary data are based on the first reference data and the auxiliary data. The probability of occurrence of the first event obtained from the data, the auxiliary data includes the scene category where the terminal is located and/or historical event statistics, the scene category includes at least one of high-speed scenes, urban scenes, mountain scenes and extreme weather scenes, historical events The statistical data includes historically occurring at least one event and reference data when each of the at least one event occurred.

Since the first reference result indicates the probability of occurrence of the first event obtained based on the first reference data, and the second reference result is used to indicate the probability of occurrence of the first event obtained based on the first reference data and the auxiliary data, it can be considered that the present application implements For example, the first reference result and the second reference result are obtained by means of prediction. Then, according to the occurrence probability of the first event, it is determined whether it is necessary to control the storage of the target data related to the first event from the volatile memory to the non-volatile memory, so as to avoid the occurrence of the first event in the vehicle as much as possible and cause driving case of data loss.

Moreover, the scene category in the above auxiliary data may be a single scene or a combination of different scenes. Therefore, the accuracy of the second reference result is further improved by including the auxiliary data of various scene categories.

Based on the first aspect, in a possible implementation manner, the target data related to the first event corresponds to the type of the first event and predefined data information.

Since there is a mapping relationship between the type of the first event and the target data related to the first event, the accuracy of the target data related to the first event obtained based on the type of the first event is high.

Exemplarily, the type of the first event includes at least one of a rear-end collision accident, an overtaking accident, a passing vehicle accident, a pedestrian accident, a non-motor vehicle accident, a vehicle failure, and a takeover accident.

The above-mentioned type of the first event E1 not only considers the traffic accidents (ie rear-end collisions, overtaking accidents, passing-vehicle accidents, pedestrian accidents and non-motor vehicle accidents), but also considers the failure of the vehicle itself and the vehicle takeover accident, that is, The type of the first event basically covers all events that may occur/occur in the vehicle. Therefore, the data management method provided in the embodiment of the present application can be applied to all scenarios where the driving data needs to be stored because of the events that may occur/occur in the vehicle, Wide range of applications.

Exemplarily, the data information includes a data type and a time interval. The data type includes at least one of sensor data, terminal state data and terminal dynamics data, and the time interval is the time interval between the first time node and the second time node.

It should be noted that the first time node is any one of the following items: the time node before the occurrence of the first event, the time node in the occurrence of the first event and the time node after the occurrence of the first event, the second time node is any one of the following: a time node before the occurrence of the first event, a time node during the occurrence of the first event, and a time node after the occurrence of the first event.

It can be understood that as long as the vehicle is in the process of the occurrence of the first event E1 (for a time interval (such as 5 seconds), which is determined by the starting time t1 and the ending time t2 when the first event E1 occurs in the vehicle), it can be considered that the first event occurred. In an event E1. Furthermore, before the occurrence of the first event E1 means before the start time t1 at which the first event E1 occurs in the vehicle, and after the occurrence of the first event E1 means after the end time t2 at which the first event E1 occurs in the vehicle.

The time interval determined according to the first time node T1 and the second time node T2 in the embodiment of the present application covers all the moments at which driving data may be generated, and further, the driving data in the corresponding time interval can be targeted according to different requirements. storage.

In a possible implementation manner, the sensor data includes one or more of the following items: the position of at least one target around the terminal, the speed of the at least one target, the volume of the at least one target, meteorological data, illumination data, Camera data or radar data.

Among them, one or more of the position of the target, the speed of the target, the volume of the target, meteorological data, illumination data or radar data can be obtained through one or more of the radar, ultrasonic radar or lidar set on the vehicle. item detected. Camera data (such as signal lights, signs, target types (can be non-motor vehicles, green belts, etc.) and target locations, etc.) can be obtained through a camera (such as a front-facing camera) installed on the vehicle.

All the above sensor data can be obtained through the sensor equipment (such as the above-mentioned radar radar, ultrasonic radar and lidar lidar, etc.) installed on the vehicle, that is, the acquisition process of the sensor data is more convenient, which improves the accuracy of the data information, and further. The accuracy of the target data TD1 related to the first event E1 is improved.

In another possible implementation manner, the terminal state data includes one or more of the following items: terminal driving speed, driving acceleration, accelerator pedal opening, brake pedal opening, steering wheel angle, steering torque or light status.

The light state may include that the vehicle is in an illuminated state (ie, the vehicle's lights (eg, headlights) are turned on), or the vehicle is in a non-illuminated state (ie, the vehicle's lights (eg, headlights) are turned off).

The above-mentioned terminal state data is the data of the vehicle itself, and the acquisition process is also convenient, which can also improve the accuracy of the data information, thereby improving the accuracy of the target data TD1 related to the first event E1.

In another possible implementation manner, the terminal dynamics data includes quality information and/or braking delay parameters of the terminal.

The mass information of the vehicle represents the weight of the vehicle, and the braking delay parameter of the vehicle is used to represent the time interval between the moment when the driver depresses the brake pedal and the moment when the vehicle starts to decelerate, which can be several milliseconds or several seconds.

In a possible implementation manner, the data management method provided by the first aspect further includes: determining that a second event occurs; controlling to store target data related to the second event from the volatile memory from the volatile memory to the in non-volatile memory;

Wherein, the second event is different from the first event, and the first reference data further includes target data related to the second event. The type of the second event includes at least one of a rear-end collision, an overtaking accident, a passing vehicle accident, a pedestrian accident, a non-motor vehicle accident, a vehicle breakdown, and a takeover accident.

It should be noted that, since the first event is different from the second event, the target data related to the first event may be different from the target data related to the second event. However, since the first event may be a combination of different types among the types of the first event introduced above, and the first event may also be a combination of different types of the types of the first event introduced above, the first event-related The target data and the target data related to the second event may also be partially identical.

It should also be noted that if the above-mentioned second event is any one of a vehicle accident (such as a rear-end collision, an overtaking accident, a passing vehicle accident, a pedestrian accident, and a non-motor vehicle accident), the vehicle itself malfunctions and the vehicle occurs Taking over events other than the accident, the embodiment of the present application does not need to store the target data related to the second event from the volatile memory into the non-volatile memory from the volatile memory.

In a possible implementation manner, the data management method provided by the first aspect further includes: performing a first process on the target data related to the first event, where the first process includes locking, encryption, time stamping, and compression. at least one.

In a possible implementation manner, if the first event does not occur within a preset period of time, the data management method provided by the first aspect further includes: performing second processing on the target data related to the first event, and the second processing Including unlocking, decrypting and/or erasing.

In a second aspect, an embodiment of the present application provides a data management apparatus. The data management apparatus includes a processor. The processor is used to: control to store the first reference data in the volatile memory, the first reference data includes target data related to the first event; control to store the target data related to the first event from the volatile memory to the non-volatile memory volatile memory.

In one possible implementation, the processor is used to:

sending indication information to the volatile memory, where the indication information is used to instruct the target data related to the first event to be transferred to the non-volatile memory; or,

The target data related to the first event is acquired from the volatile memory, and the target data related to the first event is sent to the non-volatile memory.

In a possible implementation manner, the processor is further configured to: obtain a first reference result through the event prediction model and the first reference data, where the first reference result is used to indicate the occurrence of the first event obtained based on the first reference data probability.

In a possible implementation manner, the processor is further configured to: obtain a second reference result by using the first reference data and the auxiliary data, where the second reference result is used to indicate the occurrence probability of the first event obtained based on the first reference data , the auxiliary data includes the scene category where the terminal is located and/or historical event statistics, the scene category includes at least one of high-speed scenes, urban scenes, mountain scenes and extreme weather scenes, and the historical event statistics include at least one event that occurred in history and reference data when each of the at least one event occurs.

In a possible implementation manner, the target data related to the first event corresponds to the type of the first event and pre-defined data information; wherein, the type of the first event includes rear-end collision, overtaking accident, car-meeting accident, and pedestrian accident , at least one of non-motor vehicle accidents, vehicle failures and takeover accidents, the data information includes data type and time interval, the data type includes at least one of sensor data, terminal state data and terminal dynamics data, and the time interval is the first A time interval between a time node and a second time node; wherein, the first time node is any one of the following: the time node before the occurrence of the first event, the time node in the occurrence of the first event, and the occurrence of the first event. A time node after an event, and a second time node is any one of the following: a time node before the occurrence of the first event, a time node during the occurrence of the first event, and a time node after the occurrence of the first event.

In one possible implementation, the sensor data includes one or more of the following: the position of at least one target around the terminal, the velocity of the at least one target, the volume of the at least one target, meteorological data, camera data, or Radar data.

The terminal status data includes one or more of the following: the terminal's driving speed, driving acceleration, accelerator pedal opening, brake pedal opening, steering wheel angle, steering torque, or light status.

Terminal dynamics data includes quality information and/or braking delay parameters for the terminal.

In a possible implementation manner, the processor is further configured to: determine that a second event occurs, the second event is different from the first event, and the first reference data further includes target data related to the second event; Target data of the volatile memory related to the second event is stored from the volatile memory into the non-volatile memory.

The type of the second event includes at least one of a rear-end collision, an overtaking accident, a passing vehicle accident, a pedestrian accident, a non-motor vehicle accident, a vehicle failure, and a takeover accident.

In a possible implementation manner, the processor is further configured to: perform first processing on the target data related to the first event, where the first processing includes at least one of locking, encryption, time stamping and compression.

In a possible implementation manner, if the first event does not occur within the preset first time period, the processor is further configured to: perform second processing on the target data related to the first event, where the second processing includes unlocking, Decrypt and/or Erase.

In a third aspect, an embodiment of the present application provides a data management apparatus, including an interface circuit and the processor in the second aspect above, where the interface circuit is coupled to the processor.

In a possible implementation, the interface circuit is used to connect at least one of a non-volatile memory or a volatile memory.

In a fourth aspect, an embodiment of the present application provides a terminal device, including the data management apparatus provided in the foregoing second aspect, or the data management apparatus provided in the foregoing third aspect.

In a possible implementation manner, the terminal device is an intelligent transportation device or a robot.

In a fifth aspect, embodiments of the present application provide a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer, are used to execute the first aspect and possible implementations thereof. Methods.

In a sixth aspect, an embodiment of the present application provides a computer program product, the computer program product includes instructions, and when the instructions are run on a computer or a processor, the computer or processor enables the computer or processor to implement the first aspect and possible implementations thereof method in .

It should be understood that the second to fifth aspects of the present application are consistent with the technical solutions of the first aspect of the present application, and the beneficial effects obtained by each aspect and the corresponding feasible implementation manner are similar, and will not be repeated.

Description of drawings

1 is a schematic flowchart of a data management method in an embodiment of the present application;

2 is a schematic diagram of a time interval of data information in an embodiment of the present application;

3 is a schematic diagram of a time interval of data information in an embodiment of the present application;

4 is a schematic diagram of a time interval of data information in an embodiment of the present application;

5 is a schematic diagram of a time interval of data information in an embodiment of the present application;

6 is a schematic diagram of a time interval of data information in an embodiment of the present application;

7 is a schematic diagram of a time interval of data information in an embodiment of the present application;

FIG. 8 is a schematic flowchart of obtaining a first reference result RR1 in an embodiment of the present application;

FIG. 9 is a schematic flowchart of obtaining a second reference result RR2 in an embodiment of the present application;

FIG. 10 is a schematic flowchart of the process 1000 in the embodiment of the present application;

FIG. 11 is a schematic flowchart of a process 1100 in an embodiment of the present application;

FIG. 12 is a schematic structural diagram of a data management apparatus 120 in an embodiment of the present application;

FIG. 13 is a schematic flowchart of the processor 121 implementing the storage of the target data TD1 related to the first event E1 through Mode 1 in the embodiment of the present application;

FIG. 14 is a schematic flow chart of the processor 121 implementing the storage of the target data TD1 related to the first event E1 through Mode 2 in the embodiment of the present application;

FIG. 15 is a schematic structural diagram of a data management apparatus 150 in an embodiment of the present application;

FIG. 16 is a schematic diagram of a connection between the data management device 150 and the memory in the embodiment of the present application;

FIG. 17 is a schematic diagram of another connection between the data management apparatus 150 and the memory in the embodiment of the present application.

Detailed ways

The technical solutions in the present application will be described below with reference to the accompanying drawings.

In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the accompanying drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application. , not all examples. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

The terms "first", "second", etc. in the description, embodiments and claims of the present application and the drawings are only used for the purpose of distinguishing and describing, and should not be construed as indicating or implying relative importance, nor should they be construed as indicating or implied order. Furthermore, the terms "comprising" and "having" and any variations thereof, are intended to cover non-exclusive inclusion, eg, comprising a series of steps or elements. A method, system, product or device is not necessarily limited to those steps or units expressly listed, but may include other steps or units not expressly listed or inherent to the process, method, product or device.

It should be understood that, in this application, "at least one (item)" refers to one or more, and "a plurality" refers to two or more. "And/or" is used to describe the relationship between related objects, indicating that there can be three kinds of relationships, for example, "A and/or B" can mean: only A, only B, and both A and B exist , where A and B can be singular or plural. The character "/" generally indicates that the associated objects are an "or" relationship. "At least one item(s) below" or similar expressions thereof refer to any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (a) 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.

With the rapid development of technology and the increasingly complex traffic conditions, the probability of accidents of intelligent transportation devices (such as vehicles) is also increasing. The driving data of the vehicle within a certain time interval before and after the accident, such as sensor data, vehicle state data and vehicle dynamics data, etc., play a vital role in the analysis of the cause of the accident, the restoration of the accident scene and the assessment of the responsibility of the accident. Most of the existing vehicles use a volatile memory as a buffer to store the driving data of the vehicle in a certain time interval before and after an accident in real time.

However, in the event of a traffic accident of the vehicle, a failure of the vehicle itself (such as a tire blowout due to not being replaced for a long time), or a takeover accident of the vehicle (such as an accident caused by the driver's failure to take over in time), it may be There is a situation where driving data stored in volatile memory is lost due to a power failure or malfunction of the system. Once the driving data stored in the volatile memory is lost, it will be very unfavorable for subsequent accident cause analysis, accident scene restoration and accident responsibility assessment.

In order to avoid data loss, a system for storing driving data (which can be called a data management system) requires driving data to be stored in an external memory (which can be a non-volatile memory such as a flash memory (ie flash memory) or a solid state disk (SSD). sexual memory).

In a possible implementation manner, the storage of driving data may be realized by an event data recorder (event data recorder, EDR, which is often integrated in a software module of an airbag control unit on a vehicle).

Further, the volatile memory in the accident data recorder can record the driving data of the vehicle within a certain time interval before and after the occurrence time. However, both volatile memory and accident data recorder non-volatile memory are more prone to power loss, loose interfaces, or stop working when a vehicle event occurs. Moreover, according to the storage characteristics of the volatile memory and the nonvolatile memory, when the volatile memory stops working due to a power failure or failure of the system, the driving data stored in the volatile memory will be lost. When the non-volatile memory stops working due to system power failure or failure, the driving data stored in the non-volatile memory will not be lost (that is, the driving data is still stored after the system is powered off or stopped working). in the non-volatile memory). In addition, after the non-volatile memory is re-powered, the read and write operations of the driving data stored in the non-volatile memory can continue. Non-volatile memory also has the advantages of large capacity and low cost, and can realize the storage of large-scale driving data (that is, the amount of data in the form of data is large, for example, it can be all driving data within 30 seconds before the vehicle event). .

Therefore, when an event occurs in the vehicle, the driving data stored in the volatile memory can be stored in the non-volatile memory, thereby avoiding the loss of the driving data to a certain extent.

However, when large-scale driving data needs to be stored, due to the limited write bandwidth of the non-volatile memory, a long transmission time is required to store all the driving data in the non-volatile memory. It should be noted that when a minor event (such as a minor traffic accident) occurs in the vehicle, a vehicle with relatively high stability of power supply and transmission can meet the requirement of a longer transmission time. However, once the vehicle has a serious incident (such as a serious traffic accident), the data management system will be destroyed due to environmental conditions such as high temperature and high pressure. Even for vehicles with relatively high stability of power supply and transmission, it is difficult to meet the requirements of long transmission time. Therefore, all the driving data cannot be stored in the non-volatile memory, that is, all the driving data is not stored in the non-volatile memory in time, resulting in the loss of part of the driving data.

Therefore, the above-mentioned technical solution for realizing data management through an accident data recorder is often suitable for scenarios where small-scale driving data (that is, the data volume of driving data is small), and which do not require high real-time storage.

In order to solve the technical problems existing in the technical solution of implementing data management through the accident data recorder, the embodiments of the present application may also implement the management of driving data by means of continuous storage and continuous erasing. However, since the generation rate of the driving data is relatively high (for example, the generation rate of the driving data may be 740MB/s), the scale of the driving data generated within a certain time interval is also relatively large. Therefore, a larger-capacity non-volatile memory, or a plurality of small-capacity non-volatile memories is required to realize the preservation of all driving data. In this way, the bandwidth of the non-volatile memory will be occupied for a long time, and the non-volatile memory will occupy computing resources during the encoding and decoding process. At the same time, affected by the number of times of writing and erasing driving data, the operating life of the non-volatile memory will be greatly shortened.

In order to solve the technical problems existing in the above two technical solutions (that is, the technical solution for realizing data management through an accident data recorder, and the technical solution for realizing driving data management through continuous storage and continuous erasure), the embodiments of the present application provide A data management method can effectively manage data and avoid data loss to the greatest extent.

It should be noted that the above data management method can be implemented by a terminal device (such as a vehicle), or by an electronic chip, or by other media with a data management function, and the embodiment of the present application does not limit the implementation.

In the following, taking a vehicle as an example, the data management method provided by the embodiment of the present application is introduced. A schematic flowchart of the data management method is shown in FIG. 1 , and the process 100 can be implemented through the following steps S101 and S102.

Step S101: Control to store the first reference data RD1 in a volatile memory.

In a possible implementation manner, the first reference data RD1 may include one or more of sensing data, positioning data, map data, and wireless communication technology (vehicle to everything, V2X) data. Certainly, the first reference data RD1 may also include other data, which is not limited in this embodiment of the present application.

It can be understood that any one or more of the above sensing data, positioning data, map data and wireless communication technology data can be controlled to be stored in the volatile memory. In order to improve the reliability of driving data management, data such as perception data, positioning data, map data and wireless communication technology V2X data can be stored in volatile memory.

For example, the perception data may be acquired by one or more perception devices such as a camera, a radar, an ultrasonic radar, or a lidar provided on the vehicle.

For example, whether there is a target around the vehicle can be captured by the camera.

It should be noted that the target around the vehicle may be one or multiple. In addition, the target may be a moving target, such as a moving vehicle, a moving non-motor vehicle, a walking pedestrian, and the like. The target may also be a stationary target, such as a roadside building, a tree, or a non-motor vehicle or vehicle parked on the roadside.

Since one or more targets around the vehicle can be determined by the camera, and the targets can be moving or stationary, the data management method provided by the embodiments of the present application can be applied to all traffic accidents that occur in the vehicle (for example, the vehicle and the target traffic accidents caused by collisions, or traffic accidents caused by vehicles to avoid the target, etc.).

For another example, the weather data and illumination data of the environment in which the vehicle is located may be obtained through a weather radar. The meteorological data may be rainfall intensity, snowfall intensity, cloud cover, cloud base height, etc., and the light data may be light intensity.

For another example, one or more of radar radar, ultrasonic radar and lidar lidar can be used (only radar radar, or only ultrasonic radar, or only lidar lidar, or both radar radar and laser radar can be used at the same time. The radar lidar can simultaneously detect the position of at least one target around the vehicle (which may include the position of the at least one target and the distance between the target and the vehicle) and the volume of the at least one target through radar, ultrasonic radar and lidar.

It should be noted that, when the distance between the target and the vehicle is less than a preset distance (for example, several meters, tens of meters, etc. can be set), the target can be regarded as a target around the vehicle.

It can be understood that the running speed of the vehicle, the speed of the target around the vehicle, etc. can also be obtained through radar and/or lidar, so as to obtain the running acceleration of the vehicle. When the target around the vehicle is a moving target, the moving speed of the moving target can also be obtained through lidar or radar.

Exemplarily, the positioning data can be used to locate the vehicle through a global positioning system (global positioning system, GPS) set on the vehicle, or to locate objects around the vehicle. Through the global positioning system GPS, the position of the vehicle can be located, that is, the longitude, latitude, altitude, etc. of the vehicle can be located, and the position of the target around the vehicle can be located, that is, the longitude, latitude, altitude, etc. of the target can be obtained.

It can be understood that if there are multiple targets around the vehicle, the global positioning system GPS can be used to locate the longitude, latitude, altitude, etc. where the multiple targets are located.

It is also understandable that since the target around the vehicle is a target that can be detected by one or more of radar, ultrasonic radar or lidar, the altitude of the vehicle obtained through the GPS positioning of the global positioning system is the same as the altitude of the target around the vehicle. Basically the same, the longitude and latitude of the vehicle obtained through the global positioning system GPS may be the same or different from the longitude and latitude of the target around the vehicle. For example, when the longitude and latitude of the vehicle obtained by the GPS are the same as the longitude and latitude of the target around the vehicle, it can be determined that the probability of collision between the vehicle and the target is high (if the target is a moving vehicle, the collision between the vehicle and the target occurs The first event E1 can be a car meeting accident). When the longitude and latitude of the vehicle obtained by the global positioning system GPS are different from the longitude and latitude of the target around the vehicle, it can be determined that there is a probability that the vehicle collides with the target, and the probability is small. The probability here can be determined according to the distance between the vehicle and the target. The farther the distance between the vehicle and the target, the smaller the probability of the vehicle colliding with the target. On the contrary, the closer the distance between the vehicle and the target is, the higher the probability of the vehicle colliding with the target. larger.

For example, the map data may be data such as road structures, signs, signal lights, buildings, and green belts captured by a camera.

For example, the wireless communication technology data may be transmission data between a vehicle (eg, vehicle A) and another vehicle (eg, vehicle B), vehicle A and a communication satellite, etc. obtained through a communication module installed on the vehicle.

In a possible implementation manner, since the first reference data RD1 is used to predict the probability of the occurrence of the first event E1 in the vehicle, the first reference data RD1 may include target data TD1 related to the first event RD1. Therefore, the target data TD1 related to the first event RD1 may be part of the data in the first reference data RD1, or may be all the data in the first reference data RD1, which is not limited in this embodiment of the present application.

For example, the target data TD1 related to the first event RD1 may include sensing data and positioning data, or may include sensing data, map data, and wireless communication technology data (ie, include part of the data in the first reference data RD1).

Also for example, the target data TD1 related to the first event RD1 may include sensing data, positioning data, map data, wireless communication technology data, etc. (that is, including all the data in the first reference data RD1).

Further, the above-mentioned target data TD1 related to the first event E1 may correspond to the type of the first event E1 and predefined data information. That is, the target data TD1 related to the first event E1 is determined by the type of the first event E1 and predefined data information. Since there is a mapping relationship between the type of the first event E1 and the target data TD1 related to the first event E1, the accuracy of the target data TD1 related to the first event E1 obtained based on the type of the first event E1 is high.

In a possible implementation manner, the type of the above-mentioned first event E1 may include one or more of a traffic accident of the vehicle, a failure of the vehicle itself, and a takeover accident of the vehicle.

For example, the type of the first event E1 may include a traffic accident of the vehicle, a breakdown of the vehicle itself, or a takeover accident of the vehicle. That is to say, the first event E1 that occurs in the vehicle in a certain time interval is any one of a traffic accident of the vehicle, a failure of the vehicle itself, and a takeover accident of the vehicle.

Also for example, the type of the first event E1 may include a traffic accident of the vehicle, a breakdown of the vehicle itself, and a takeover accident of the vehicle. That is to say, the first event E1 that occurs in a vehicle in a certain time interval may be all of a traffic accident in the vehicle, a failure in the vehicle itself, and a takeover accident in the vehicle (that is, a traffic accident and a takeover accident in the vehicle occur at the same time in a certain time interval). , and a failure occurred).

The above-mentioned type of the first event E1 not only considers the traffic accident that occurs in the vehicle, but also considers the failure of the vehicle itself and the vehicle takeover accident, that is, the type of the first event E1 basically covers all events that may occur/occur in the vehicle. Therefore, The data management method provided by the embodiment of the present application can be applied to all scenarios in which driving data needs to be stored due to possible occurrence/occurrence of events in the vehicle, and has a wide range of applications.

For another example, the type of the first event E1 may further include a traffic accident and a takeover accident of the vehicle. That is to say, the first event E1 that occurs in a vehicle in a certain time interval may be part of a traffic accident of the vehicle, a failure of the vehicle itself, and a takeover accident of the vehicle (that is, the vehicle has a traffic accident and a takeover accident at the same time in a certain time interval. accident, the vehicle itself did not malfunction).

Optionally, the traffic accident that occurs in the vehicle may be any one or more of a rear-end collision accident, an overtaking accident, a passing vehicle accident, a pedestrian accident, and a non-motor vehicle accident.

For example, a rear-end collision accident occurs due to a rear-end collision of a vehicle during driving, or a collision accident occurs when a vehicle meets a vehicle.

For another example, a vehicle (such as vehicle A) has a rear-end collision with another vehicle (such as vehicle B), and at the same time, the vehicle (such as vehicle A) collides with a non-motor vehicle (such as a non-motor vehicle) around the vehicle (such as vehicle A) due to the rear-end collision. a target around the vehicle). In this case, it can be considered that vehicle A has both a rear-end collision accident and a non-motor vehicle accident at the same time.

It can be understood that the above-mentioned rear-end collision accident may be a traffic accident in which the front of vehicle A collides with the rear of vehicle B when vehicles (eg, vehicle A and vehicle B) running in the same lane follow behind.

It is also understandable that the above-mentioned pedestrian accident may be a traffic accident with the vehicle A due to the pedestrian's negligence or violation of traffic rules.

It can also be understood that the above-mentioned non-motor vehicle accident may be a traffic accident between a non-motor vehicle (which may be a bicycle or an electric bicycle) and the vehicle A.

It should be noted that, except for the above rear-end collision accident and non-motor vehicle accident, other traffic accidents that occur in vehicles are not introduced one by one in the embodiments of the present application.

Optionally, the failure of the vehicle itself may be that the tires of the vehicle are punctured because they have not been replaced for a long time, or the braking system of the vehicle fails.

Optionally, the takeover accident that occurs in the vehicle may be an accident caused by the driver's failure to take over in time, or the like.

In a possible implementation manner, the above-mentioned data information may include a data type and a time interval.

Illustratively, the aforementioned data types may include one or more of sensor data, terminal status data, and terminal dynamics data. In order to obtain more accurate target data TD1 related to the first event E1, the embodiment of the present application uses sensor data, terminal state data, and terminal dynamics data as examples to introduce data types.

For example, the sensor data may include the position of a target around the vehicle (in this embodiment of the present application, there is a target around the vehicle, such as target A), the position, the speed of target A, the volume of target A, weather data, illumination data, camera data and One or more of the radar data. The embodiments of the present application introduce sensor data including the position of the target A around the vehicle, the speed of the target A, the volume of the target A, meteorological data, illumination data, camera data, and radar data as examples.

It can be understood that the sensor data may include the speed of the target A regardless of whether the target A around the vehicle is a stationary target or a moving target. It should be noted that when the target A is a stationary target, the speed of the target A is 0.

In a possible implementation manner, the position of the target A (including the position of the target A (for example, the target A is located in the front left of the vehicle) and the distance between the target A and the vehicle), the speed of the target A, the volume of the target A, One or more of weather data, illumination data or radar data may be detected by one or more of radar, ultrasonic radar or lidar provided on the vehicle. That is to say, the position of target A, the speed of target A, the volume of target A, meteorological data, illumination data and radar data can be detected only by radar radar, or the position of target A can be detected only by ultrasonic radar or lidar lidar, The speed of target A, the volume of target A, meteorological data, illumination data and radar data can also be detected through radar radar to detect the position of target A, the speed of target A, the volume of target A, and the lidar meteorological data, illumination can be detected through lidar Data and radar data, the embodiments of the present application do not limit the detection method of sensor data.

It can be understood that, regardless of whether the target A around the vehicle is a stationary target or a moving target, radar, ultrasonic radar or lidar can detect the position and volume of target A.

In another possible implementation manner, the above-mentioned camera data (such as signal lights, signs, types of targets (which can be non-motor vehicles, green belts, etc.) and the position of target A, etc.) can be obtained through the camera ( such as the front camera).

For another example, the terminal state data may include one or more of the vehicle's running speed, running acceleration, accelerator pedal opening, brake pedal opening, steering wheel angle, steering torque, and light state. Similarly, in order to obtain more accurate target data TD1 related to the first event E1, the terminal state data in this embodiment of the present application includes the vehicle's running speed, running acceleration, accelerator pedal opening, brake pedal opening, and steering wheel angle , steering torque and light status. The light state may include that the vehicle is in an illuminated state (ie, the vehicle's lights (eg, headlights) are turned on), or the vehicle is in a non-illuminated state (ie, the vehicle's lights (eg, headlights) are turned off).

As another example, the terminal dynamics data includes vehicle mass information and/or braking delay parameters. The mass information of the vehicle represents the weight of the vehicle, and the braking delay parameter of the vehicle is used to represent the time interval between the moment when the driver depresses the brake pedal and the moment when the vehicle starts to decelerate, which can be several milliseconds or several seconds.

The above-mentioned terminal dynamics data can be obtained by referring to the relevant data of the vehicle (such as the description of the vehicle, etc.), which provides a reliable way to obtain the target data TD1 related to the first event E1.

For example, the time interval of the above data information may be the time interval between the first time node (represented by T1 ) and the second time node (represented by T2 ).

The above-mentioned first time node T1 may be a time node before the occurrence of the first event E1, a time node in the occurrence of the first event E1, or a time node after the occurrence of the first event E1. Similarly, the above-mentioned second time node T2 may also be a time node before the occurrence of the first event E1, a time node during the occurrence of the first event E1, or a time node after the occurrence of the first event E1.

The following describes the time interval and the data stored in the corresponding time interval:

Case 1: As shown in FIG. 2 , the first time node T1 is the time node before the first event E1 occurs, and the second time node T2 is the time node after the first event E1 occurs. In this case, the target data related to the first event E1 covers the data corresponding to the time interval between the time node before the occurrence of the first event E1 and the time node after the occurrence of the first event E1. It can be understood that the data in the time interval between the time node before the occurrence of the first event E1 and the time node after the occurrence of the first event E1 (that is, the data in the first reference data RD1 related to the first event E1 in this case) is controlled. The objective function TD1) is stored in volatile memory.

Case 2: As shown in FIG. 3 , the first time node T1 is the time node before the first event E1 occurs, and the second time node T2 is the time node in which the first event E1 occurs. In this case, the target data related to the first event E1 covers the data corresponding to the time interval between the time node before the occurrence of the first event E1 and the time node in which the first event E1 occurs, but does not cover the occurrence of the first event The data corresponding to the time node after E1. It can be understood that the data corresponding to the time interval between the time node before the occurrence of the first event E1 and the time node in the occurrence of the first event E1 (that is, in the first reference data RD1 in this case and the first event E1 in this case) are controlled. The related objective function TD1) is stored in the volatile memory, and there is no need to control the data corresponding to the time node after the occurrence of the first event E1 to be stored in the volatile memory.

Case 3: As shown in FIG. 4 , the first time node T1 is the time node before the first event E1 occurs, and the second time node T2 is the time node before the first event E1 occurs. That is to say, both the first time node T1 and the second time node T2 are time nodes before the occurrence of the first event E1. In this case, the target data TD1 related to the first event E1 covers the data corresponding to the time interval between the two time nodes before the occurrence of the first event E1, but does not cover the data corresponding to the time nodes in the occurrence of the first event E1 Data corresponding to the time node after the occurrence of the first event E1. It can be understood that the control will store the data corresponding to the time interval between the two time nodes before the first event E1 (that is, the objective function TD1 related to the first event E1 in the first reference data RD1 in this case) to The volatile memory does not need to control to store the data corresponding to the time node in the first event E1 and the data corresponding to the time node after the first event E1 in the volatile memory.

Case 4: As shown in FIG. 5 , the first time node T1 is the time node in which the first event E1 occurs, and the second time node T2 is the time node in which the first event E1 occurs. That is to say, both the first time node T1 and the second time node T2 are time nodes in which the first event E1 occurs. In this case, the target data TD1 related to the first event E1 covers the data corresponding to the time interval between two time nodes in the occurrence of the first event E1, but does not cover the data corresponding to the time node before the occurrence of the first event E1 Data corresponding to the time node after the occurrence of the first event E1. It can be understood that the control will store the data corresponding to the time interval between the two time nodes in the first event E1 (that is, the objective function TD1 related to the first event E1 in the first reference data RD1 in this case) to The volatile memory does not need to control the storage of the data corresponding to the time node before the first event E1 and the data corresponding to the time node after the first event E1 in the volatile memory.

Case 5: As shown in FIG. 6 , the first time node T1 is the time node in which the first event E1 occurs, and the second time node T2 is the time node after the first event E1 occurs. In this case, the target data TD1 related to the first event E1 covers the data corresponding to the time interval between the time node in which the first event E1 occurs and the time node after the occurrence of the first event E1. It can be understood that the data corresponding to the time interval between the time node in the first event E1 and the time node after the occurrence of the first event E1 (that is, in the first reference data RD1 in this case and the first event E1 in this case) are controlled. The related objective function TD1) is stored in the volatile memory, and there is no need to control the storage of the data corresponding to the time node before the occurrence of the first event E1 in the volatile memory.

Case 6: As shown in FIG. 7 , the first time node T1 is the time node after the first event E1 occurs, and the second time node T2 is the time node after the first event E1 occurs. That is to say, both the first time node T1 and the second time node T2 are time nodes after the occurrence of the first event E1. In this case, the target data TD1 related to the first event E1 covers the data corresponding to the time interval between two time nodes after the occurrence of the first event E1, but does not cover the data corresponding to the time node before the occurrence of the first event E1 Data corresponding to the time node in which the first event E1 occurs. It can be understood that the control stores the data corresponding to the time interval between the two time nodes after the occurrence of the first event E1 (that is, the objective function TD1 related to the first event E1 in the first reference data RD1 in this case) to The volatile memory does not need to control to store the data corresponding to the time node before the first event E1 and the data corresponding to the time node in the first event E1 to the volatile memory.

Step S102: Control to store the target data TD1 related to the first event E1 from the volatile memory to the non-volatile memory.

Further, in this embodiment of the present application, step S102 may be performed in the following two ways:

Mode 1: Send the indication information CI1 to the volatile memory.

Exemplarily, the above-mentioned indication information CI1 is used to instruct to transmit the target data TD1 related to the first event E1 to the non-volatile memory.

In a possible implementation manner, the processor of the vehicle (which may be the processor 121 or the processor 152 below) sends the indication information CI1 to the volatile memory, and then the volatile memory receives the indication information CI1, and sends the first indication information CI1 to the volatile memory. The target data TD1 related to an event E1 is transferred to the non-volatile memory for storage.

It can be understood that the processor only plays the role of sending the indication information CI1 to the volatile memory in Mode 1. After the volatile memory receives the indication information CI1, the processor no longer participates in the process that the volatile memory transmits the target data TD1 related to the first event E1 to the non-volatile memory for saving. Thus, the processor in approach 1 is coupled with volatile memory, and the volatile memory is coupled with non-volatile memory, and the processor is not coupled with non-volatile memory.

Mode 2: Acquire the target data TD1 related to the first event E1 from the volatile memory, and send the target data TD1 related to the first event E1 to the non-volatile memory.

In a possible implementation manner, the processor first acquires the target data TD1 related to the first event E1 stored in the volatile memory in step S101, and then the processor acquires the acquired target data TD1 related to the first event E1 Data TD1 is sent to non-volatile memory for storage.

It can be understood that the processor participates in the whole process in Mode 2, and plays a leading role. That is, the processor not only plays the role of acquiring the target data TD1 related to the first event E1 from the volatile memory, but also plays the role of sending the acquired target data TD1 related to the first event E1 to the non-volatile memory the role of preservation.

Thus, the processor in Approach 2 is coupled with volatile memory, and coupled with non-volatile memory, and volatile memory is not coupled with non-volatile memory.

Of course, step S102 may also be performed in other manners than the above two manners, which are not limited in this embodiment of the present application.

Both the above-mentioned methods 1 and 2 can manage driving data of any scale (that is, there is no limit to the amount of driving data), and there is no requirement for the real-time storage of driving data (that is, it is not only applicable to the real-time storage of driving data). It can also be applied to scenarios with high real-time storage requirements). In addition, the embodiments of the present application do not need to perform multiple erasing of the non-volatile memory, etc., and can prolong the operating life of the non-volatile memory.

Before the above step S101, the embodiment of the present application can also predict the probability of the occurrence of the first event E1 in the vehicle according to the above-mentioned first reference data RD1, that is, obtain the occurrence probability P of the first event E1 according to the above-mentioned first reference data RD1.

In a possible implementation manner, the occurrence probability P of the first event E1 can be obtained through the following process 1 and process 2, and then, according to the occurrence probability P of the first event E1, it is determined whether it is necessary to control whether it is necessary to control the connection between the volatile memory and the first event. The target data TD1 related to an event E1 is stored in the non-volatile memory, so as to avoid the situation that the driving data is lost due to the occurrence/occurrence of the first event E1 in the vehicle as much as possible.

Process 1: Through the event prediction model EFM and the first reference data RD1 (see related description above), the first reference result RR1 can be obtained.

The event prediction model EFM may indicate a mapping relationship between the first reference data RD1 and the first reference result RR1, which can be obtained by training historical statistical data.

The first reference result RR1 may indicate the occurrence probability P ₁ of the first event E1 obtained based on the first reference data RD1.

As shown in FIG. 8 , the first reference data RD1 can be used as the input of the event prediction model EFM, that is, the first reference data RD1 can be input into the event prediction model EFM, and the first event indicating the first event obtained based on the first reference data RD1 can be obtained. The first reference result RR1 of the probability of occurrence of E1.

Further, the obtained occurrence probability P ₁ of the first event E1 may be 0, 0.1, 0.5, or 0.9, etc. It can be understood that the occurrence probability P1 _of the _first event E1 is 0, indicating that the first event E1 is unlikely to occur in the vehicle. sex is greater.

Further, the occurrence probability threshold value P _{set of the first event E1 may be set, for example, the occurrence probability threshold value P set} _of the first event E1 may be set to 0.3. Next, when the occurrence probability P ₁ of the first event E1 obtained through the process 1 is greater than or equal to 0.3 (ie, P ₁ ≥ 0.3), it indicates that the vehicle has a high possibility of the first event E1, and it can be considered that the vehicle will have the first event. E1. In this case, the storage of the target data TD1 related to the first event E1 can be realized through the above-mentioned steps S101 and S102 in time.

Process 2: Through the first reference data RD1 (refer to the relevant description above) and the auxiliary data AD, the second reference result RR2 can be obtained.

Wherein, the auxiliary data AD may include the scene category where the vehicle is located, or include historical event statistical data. Of course, the auxiliary data AD may also include the scene category where the vehicle is located and historical event statistical data.

It should be noted that, in addition to the above-mentioned scene category where the vehicle is located and historical event statistical data, the auxiliary data AD may also include other data, which is not limited in this application.

Optionally, the scene category may include any one or more of a high-speed scene, an urban scene, a mountain scene, and an extreme weather scene.

For example, high-speed scenarios may include high-speed ramp scenarios (vehicles traveling on high-speed ramps), high-speed straight-track scenarios (vehicles traveling on straights without inclines and curvatures), high-speed curve scenarios (vehicles traveling on high-speed curves), and high-speed ramps Scenario (vehicle driving on a high-speed ramp), etc.

For another example, the urban area scene may include an intersection scene and a non-intersection scene, and the like. The intersection scene may be an intersection scene (the vehicle is driving at the intersection) or the T-junction scene (the vehicle is driving at the T-junction). The non-intersection scene can be a one-way street scene (vehicle driving on a one-way street) or a two-way street scene (vehicle driving on a two-lane road) in an urban road.

Also for example, the mountain scene may include a mountain curve scene (the vehicle is driving on a curve in the mountain), a mountain narrow road scene (the vehicle is driving on a narrow road in the mountain), and a mountain ramp scene (the vehicle is driving on a slope in the mountain). )Wait.

For another example, the extreme weather scenario may include heavy rain, heavy snow, hail, typhoon, sandstorm, fog, and the like.

In one example, when the vehicle is traveling on a high-speed straight road and encounters heavy snow, the scene categories may include high-speed scenes and extreme weather scenes.

In another example, when the vehicle is driving on a mountain curve and encounters a typhoon, the scene category may include a mountain scene and an extreme weather scene.

It can be seen from the above introduction that the scene category in the auxiliary data AD can be a single scene or a combination of different scenes. Therefore, the accuracy of the second reference result ED2 is further improved by the auxiliary data AD including various scene categories.

Optionally, the historical event statistics data may include one event or multiple events that have occurred in history, and reference data when each event of the one event or multiple events occurs.

It should be noted that the historical event statistics may be acquired from a processor outside the vehicle, or acquired from a processor inside the vehicle (ie, the above processor).

In one example, one or more events that have occurred in history in the historical event statistics may be other than the above vehicle (the above vehicle is a vehicle for which the first event E1 may be predicted to occur, such as vehicle A) The other vehicles (which can be multiple vehicles, such as vehicle 1 to vehicle N) have once (ie history) an event or multiple events. It should be noted that, it may be an event that has occurred in each of vehicles 1 to N, or multiple events that have occurred in each of vehicles 1 to N, which is not limited in this embodiment of the present application.

In another example, the reference data in the historical event statistics data when one event or each of the multiple events occurs may be the perception data, positioning, and positioning of the vehicle 1 to the vehicle N when each of the multiple events occurs. One or more of data, map data, and wireless communication technology data. For the introduction of perception data, positioning data, map data, and wireless communication technology data, reference may be made to the above, and details are not described herein again in this embodiment of the present application.

The second reference result RR2 in the above process 2 may indicate the occurrence probability P ₂ of the first event E1 obtained based on the first reference data RD1 and the auxiliary data AD.

Referring to the above process 1, the obtained occurrence probability P2 of the first event E1 may also be ₀ , 0.1, 0.5, or 0.9, etc. It can be understood that the occurrence probability _P2 of the first event E1 is ₀ , indicating that the first event E1 is unlikely to occur in the vehicle. sex is greater.

Still referring to the above process 1, the occurrence probability threshold P _{set of the first event E1 may be set, for example, the occurrence probability threshold P set} _of the first event E1 may be set to 0.3. Next, when the occurrence probability P ₂ of the first event E1 obtained through the process 1 is greater than or equal to 0.3 (ie, P ₂ ≥ 0.3), it indicates that the vehicle has a high possibility of the occurrence of the first event E1, and it is necessary to pass the above steps S101 and S101 in time. S102 realizes the storage of the target data TD1 related to the first event E1.

Continuing to refer to the above process 1, as shown in FIG. 9, the first reference data RD1 and the auxiliary data AD can be used as the input of the event prediction model EFM, that is, the first reference data RD1 and the auxiliary data AD are input into the event prediction model EFM, that is A second reference result RR2 indicating the probability of occurrence of the first event E1 based on the first reference data RD1 and the auxiliary data AD can be obtained.

In a possible implementation manner, after the above step S102, when the event that actually occurs in the vehicle (ie the second event E2) is different from the first event E1 above, the embodiment of the present application may also control the The target data TD2 related to the event E2 is stored in the non-volatile memory. As shown in FIG. 10, the process 1000 can be implemented through the following steps S103a and S103b.

Step S103a: Determine the second event E2 that actually occurs in the vehicle.

The second event E2 is an event that actually occurs in the vehicle, and the first event E1 is an event that is predicted to occur in the vehicle.

You can refer to the relevant introduction of the type of the first event E1 above, and the second event E2 can also be a traffic accident of a vehicle (for example, a rear-end collision, an overtaking accident, a passing vehicle accident, a pedestrian accident, or any one of a non-motor vehicle accident), One or more of the failure of the vehicle itself and the takeover of the vehicle. However, the second event E2 is different from the first event E1 above.

For example, the first event E1 is that a vehicle has a traffic accident (for example, any one of a rear-end collision, an overtaking accident, a passing-vehicle accident, a pedestrian accident, and a non-motor vehicle accident), and the second event E2 is a vehicle failure (refer to the above introduction).

For another example, the first event E1 is a traffic accident of the vehicle, and the second event E2 is a takeover accident of the vehicle (refer to the above description).

For another example, the first event E1 is a rear-end collision accident of the vehicle, and the second event E2 is a non-motor vehicle accident of the vehicle. In this case, although the rear-end collision accident and the non-motor vehicle accident are both traffic accidents caused by vehicles, it can still be determined that the first event E1 is different from the second event E2.

Of course, it can also be that the first event E1 is the failure of the vehicle, and the second event E2 is other situations such as a vehicle takeover accident. This embodiment of the present application does not list them all here, as long as the first event E1 and the second event E2 different.

In a possible implementation manner, in addition to the above-mentioned target data TD1 related to the first event E1, the first reference data RD1 also contains the target data TD2 related to the second event E2.

Referring to the above target data TD1 related to the first event RD1, the target data TD2 related to the second event E2 may also be part of the data in the first reference data RD1, or may be all of the first reference data RD1. data.

For example, the target data TD2 related to the second event E2 may also include sensing data and positioning data, or may include sensing data, map data, and wireless communication technology data (ie, include part of the data in the first reference data RD1).

Also for example, the target data TD2 related to the first event RD1 may include sensing data, positioning data, map data and wireless communication technology data (that is, including all the data in the first reference data RD1).

It should be noted that, since the first event E1 is different from the second event E2, the target data TD1 related to the first event RD1 may be different from the target data TD2 related to the second event E2. However, since the first event E1 may be a combination of different types of the types of the first event E1 introduced above, the first event E2 may also be a combination of different types of the types of the first event E2 introduced above, so it is different from the first event E2. The target data TD1 related to an event RD1 and the target data TD2 related to the second event E2 may also be partially identical.

For example, the first event E1 is that the vehicle has a rear-end collision accident and the vehicle is faulty, and the second event E2 is that the vehicle has a non-motor vehicle accident and the vehicle is faulty. In this case, the first event E1 is different from the second event E2, but since there is a vehicle failure in both the first event E1 and the second event E2, the target data TD1 related to the first event RD1 and the The target data TD2 related to the two events E2 may be partially the same.

It can be understood that the data related to the failure of the vehicle in the target data TD1 related to the first event RD1 may be the same as the data related to the failure of the vehicle in the target data TD2 related to the second event E2, and are related to the first event E1. The data related to the rear-end collision accident in the target data TD1 of , and the data related to the non-motor vehicle accident in the target data TD2 related to the second event E2 are different.

For another example, the first event E1 is that the vehicle has a rear-end collision accident and an overtaking accident at the same time (that is, the vehicle has an overtaking accident during the overtaking process. At the same time, the vehicle also has a rear-end collision accident due to overtaking), and the second event E2 is that the vehicle has a rear-end collision accident. Overtaking accidents and non-motor vehicle accidents occur at the same time (that is, the vehicle has an overtaking accident during the process of overtaking. At the same time, due to overtaking, the vehicle has a non-motor vehicle accident (it can be that the vehicle needs to overtake and the speed is too fast, and then hit a non-motor vehicle). In this case, the first event E1 and the second event E2 are also different, but since there is an overtaking accident by a vehicle in both the first event E1 and the second event E2, it is different from the first event E1. The associated target data TD1 and the target data TD2 associated with the second event E2 may then be partially identical.

It can be understood that the data related to the overtaking accident in the target data TD1 related to the first event E1 may be the same as the data related to the overtaking accident in the target data TD2 related to the second event E2, and the data related to the first event RD1. The data related to the rear-end collision accident in the target data TD1 is different from the data related to the non-motor vehicle accident in the target data TD2 related to the second event E2.

Step S103b: Control to store the target data TD2 related to the second event E2 from the volatile memory into the non-volatile memory from the volatile memory.

In a possible implementation manner, referring to step S102 above, step S103b can be performed in the following two manners.

Mode A: Send the indication information CI2 to the volatile memory.

Exemplarily, the above-mentioned indication information CI2 is used to instruct to transmit the target data TD2 related to the second event E2 to the non-volatile memory.

Further, the processor of the vehicle sends the indication information CI2 to the volatile memory, and then the volatile memory receives the indication information CI2, and transmits the target data TD2 related to the second event E2 to the non-volatile memory for saving.

It can be understood that, in the mode A, the processor only plays the role of sending the indication information CI2 to the volatile memory. After the volatile memory receives the indication information CI2, the processor no longer participates in the process that the volatile memory transmits the target data TD2 related to the second event E2 to the non-volatile memory for saving. Thus, the processor in approach A is coupled with volatile memory, and the volatile memory is coupled with non-volatile memory, and the processor is not coupled with non-volatile memory.

Mode B: Acquire the target data TD2 related to the second event E2 from the volatile memory, and send the target data TD2 related to the second event E2 to the non-volatile memory.

Further, the processor first acquires the target data TD2 related to the second event E2 stored in the volatile memory, and then the processor sends the acquired target data TD2 related to the second event E2 to the non-volatile memory. to save.

It can be understood that the processor participates in the whole process in the way B, and plays a leading role. That is, the processor not only plays the role of acquiring the target data TD2 related to the second event E2 from the volatile memory, but also plays the role of sending the acquired target data TD2 related to the second event E2 to the non-volatile memory the role of preservation.

Thus, the processor in approach B is coupled with volatile memory, and coupled with non-volatile memory, and volatile memory is not coupled with non-volatile memory.

Of course, step S103a2 may also be performed in other manners than the above two manners, which is not limited in this embodiment of the present application.

It should be noted that, if the above-mentioned second event E2 is any one of a vehicle accident (such as a rear-end collision, an overtaking accident, a passing-vehicle accident, a pedestrian accident, and a non-motor vehicle accident), a failure of the vehicle itself, and the occurrence of a vehicle accident. Taking over other events other than the accident, the embodiment of the present application does not need to store the target data TD2 from the volatile memory related to the second event E2 from the volatile memory to the non-volatile memory.

After step S102 in this embodiment of the present application, one or more processes of locking, encryption, time stamping and compression may also be performed on the target data TD1 related to the first event E1.

Further, after one or more of locking, encrypting, time stamping and compressing the target data TD1 related to the first event E1, if within a preset time period (such as 24 hours), the vehicle When the first event E1 does not occur, one or more processes of unlocking, decrypting and erasing the target data TD1 related to the first event E1 in the non-volatile memory may be performed.

FIG. 11 is another schematic flowchart of the data management method provided by the embodiment of the present application. As shown in FIG. 11, process 1100 can be implemented by the following steps.

Step S1101: Obtain the occurrence probability P of the first event E1 according to the first reference data RD1.

Step S1102: Control to store the first reference data RD1 in the volatile memory.

Step S1103: Control to store the target data TD1 related to the first event E1 from the volatile memory to the non-volatile memory.

Step S1104: Perform a first process (such as one or more processes of locking, encryption, time stamping and compression) on the target data TD1 related to the first event E1.

Step S1105: Determine whether the second event E2 is the same as the first event, if so, go to step S1106, otherwise, go to step S1107.

Step S1106: Control to store the target data TD2 related to the second event E2 in the non-volatile memory.

Step S1107: If the vehicle does not have the first event E1 after a preset period of time (such as 24 hours), perform second processing (such as unlocking, decrypting) on the target data TD1 related to the first event E1 in the non-volatile memory. , erase one or more of the processes).

In a possible implementation manner, the detailed execution process of steps S1101 to S1107 may be implemented with reference to the above related introduction.

It should be noted that, in step S1107, if the first event E1 does not occur in the vehicle within a preset time period (such as 24 hours), it means that the vehicle is even less likely to occur the first event E1 after the preset time period (such as 24 hours). In an event E1, then, one or more processes of unlocking, decrypting, and erasing the target data TD1 in the non-volatile memory related to the first event E1 can be performed.

FIG. 12 is a schematic structural diagram of a data management apparatus provided by an embodiment of the present application. As shown in FIG. 12 , the data management device 120 may be a device with a data management function, such as a management chip, a vehicle, a management chip in a vehicle, and the like. The data management apparatus 120 may include a processor 121 .

In a possible implementation manner, the processor 121 is configured to control to store the first reference data RD1 in the volatile memory, and control to store the target data TD1 related to the first event E1 from the volatile memory to the volatile memory. non-volatile memory.

Exemplarily, as in step S101, the first reference data RD1 also includes target data TD1 related to the first event E1. For the detailed introduction of the first reference data RD1 and the target data TD1 related to the first event E1, reference may be made to the foregoing description, which will not be repeated in this embodiment of the present application.

Further, the processor 121 may also store the target data TD1 related to the first event E1 from the volatile memory to the non-volatile memory through the following two controls.

Mode 1: Send the indication information CI1 to the volatile memory.

It should be noted that, for the detailed implementation process of Mode 1 and Mode 2, reference may be made to the above related introduction, which is not repeated in this embodiment of the present application.

In one possible implementation, the processor 121 may include a prediction module 1211 and a processing module 1212 .

Exemplarily, the processor 121 implements the storage of the target data TD1 related to the first event E1 through the above method 1. As shown in FIG. 13 , the prediction module 1211 can, according to the first reference data RD1 and auxiliary data AD (including scene categories and historical event statistics, refer to the above introduction) from the related equipment on the vehicle, through the above process 1 or Process 2 (refer to the introduction above) obtains the occurrence probability P for indicating the first event.

After the prediction module 1211 obtains the occurrence probability P used to indicate the first event, the processing module 1212 sends the indication information CI to the control volatile memory A according to the occurrence probability P used to indicate the first event, and the volatile memory A receives the information CI. After receiving the indication information AI from the processing module 1212, the volatile memory A stores the target data TD1 related to the first event E1 to the non-volatile memory B.

Of course, the processor 121 can also implement the storage of the target data TD1 related to the first event E1 through the above manner 2. As shown in FIG. 14 , the prediction module 1211 can, according to the first reference data RD1 and auxiliary data AD (including scene categories and historical event statistics, refer to the above introduction) from the related equipment on the vehicle, through the above process 1 or Process 2 (refer to the introduction above) obtains the occurrence probability P for indicating the first event.

After the prediction module 1211 obtains the occurrence probability P used to indicate the first event, the processing module 1212 obtains the target data TD1 related to the first event E1 from the volatile memory A according to the occurrence probability P used to indicate the first event, And the target data TD1 related to the first event E1 is sent to the non-volatile memory B.

In a possible implementation manner, after the processor 121 controls the target data TD1 related to the first event E1 from the volatile memory to be stored in the non-volatile memory, the processor 121 can control the target data TD1 related to the first event E1 to be stored in the non-volatile memory. The target data TD1 is locked, encrypted, time stamped and compressed.

After one or more of the above-mentioned processing of locking, encrypting, time stamping and compressing the target data TD1 related to the first event E1, if the vehicle actually occurs a second event E2 that is different from the first event E1 , the processor 121 controls the target data TD2 related to the second event E2 to be stored in the non-volatile memory.

In another possible implementation manner, if the vehicle does not have the first event E1 after a preset period of time (eg, 24 hours), the processor 121 stores the target data related to the first event E1 in the non-volatile memory TD1 performs one or more processes of unlocking, decrypting, and erasing.

It should be noted that, for the detailed process of controlling the storage of the target data TD2 related to the second event E2 in the non-volatile memory, reference may be made to the above related introduction, which will not be repeated in this embodiment of the present application.

FIG. 15 is a schematic structural diagram of a data management apparatus provided by an embodiment of the present application. As shown in FIG. 15 , the data management apparatus 150 includes an interface circuit 151 and at least one processor 152. Interface circuit 151 is coupled to processor 152 .

In a possible implementation manner, if the processor 152 implements storing the target data TD1 related to the first event E1 in the non-volatile memory through the above manner 1, the interface circuit 151 may include an interface circuit 1511 . The interface circuit 1511 is coupled with volatile memory.

It can be understood that since the processor 152 sends the indication information CI1 to the volatile memory through 1511, the indication information CI1 is used to instruct the volatile memory to transfer the target data TD1 related to the first event E1 to the non-volatile memory. , so, as shown in FIG. 16 , the processor 152 is only coupled with the volatile memory A through the interface circuit 151 , and the volatile memory A is coupled with the non-volatile memory B.

In another possible implementation manner, if the processor 152 stores the target data TD1 related to the first event E1 in the non-volatile memory through the foregoing manner 2, as shown in FIG. 17 , the interface circuit 151 may include an interface circuit 1511 and interface circuit 1512. The processor 152 is coupled to the volatile memory A through the interface circuit 1511 , and is coupled to the non-volatile memory B through the interface circuit 1512 .

It should be noted that the processor 152 in the data management apparatus 150 is the same as the processor 121 in the data management apparatus 120, and both are used to execute the above-mentioned steps 101 and S102.

In the process of implementation, the processor mentioned in the above embodiments may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable Field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the methods disclosed in the embodiments of the present application may be directly embodied as executed by a hardware coding processor, or executed by a combination of hardware and software modules in the coding processor. The software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art. The storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware.

The non-volatile memory mentioned in the above embodiments can be read-only memory (read-only memory, ROM), programmable read-only memory (programmable ROM, PROM), erasable programmable read-only memory (erasable PROM) , EPROM), electrically erasable programmable read-only memory (electrically EPROM, EEPROM), flash memory (ie flash memory), or solid state disk (solid state disk, SSD). Volatile memory may be random access memory (RAM), which acts as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (synchlink DRAM, SLDRAM) ) and direct memory bus random access memory (direct rambus RAM, DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but not be limited to, these and any other suitable types of memory.

The technical effects achieved by the above data management apparatus 120 and the data management apparatus 150 are similar to the above data management methods, and will not be repeated here.

In a possible implementation manner, an embodiment of the present application provides a terminal device, and the terminal device may include the data management apparatus 120 or the data management apparatus 150 mentioned in the foregoing embodiments.

In an example, the terminal device may be an intelligent transportation device (that is, a vehicle in this embodiment of the present application), and may also be a robot.

In another possible implementation manner, an embodiment of the present application provides a computer-readable storage medium, where an instruction is stored in the computer-readable storage medium, and when the instruction is run on a computer, it is used to execute and implement the above-mentioned embodiments. The data management method of the data management device 120 in the above, or the data management method of the data management device 150 in the above-mentioned embodiment is executed.

In yet another possible implementation manner, the embodiment of the present application provides a computer program product, and the computer program product contains instructions, when the instructions are executed on a computer or a processor, the computer or the processor is made to implement the above-mentioned embodiments. The data management method of the data management apparatus 120, or the data management method of the data management apparatus 150 in the above-mentioned embodiment is implemented.

It should be noted that all the above embodiments can be applied to vehicles. The vehicle may be an autonomous vehicle (autonomous vehicle), or a vehicle driven by a driver, or may be a vehicle of other forms, and the embodiment of the present application does not limit the form of the vehicle.

It is understandable that an autonomous vehicle, also known as a driverless car, a computer-driven car or a wheeled mobile robot, is a vehicle that relies on artificial intelligence (AI), visual computing (computational theory), radar, lidar, The monitoring device and the global positioning system GPS can realize the unmanned intelligent car through the computer system without any human active operation. A vehicle driven by a driver is relatively common, and will not be further described in this embodiment of the present application.

It is also understandable that the driving data that needs to be stored is often proportional to the degree of vehicle automation, that is to say, the higher the degree of automation of the self-driving vehicle, the larger the scale of driving data that needs to be stored. For example, current partially autonomous vehicles can generate several terabytes or even hundreds of terabytes of data per day. The above-mentioned embodiments of the present application are suitable for managing large-scale driving data generated by autonomous driving vehicles and scenarios with high requirements for real-time storage. Of course, it is also suitable for managing small-scale driving data generated by vehicles, as well as scenarios that do not require high real-time storage requirements. It should be noted that, in addition to the above scenarios, the above embodiments of the present application may also be applied to other scenarios where driving data needs to be stored, and the embodiments of the present application do not limit the applicable scenarios.

It should be understood that, in various embodiments of the present application, the size of the sequence numbers of the above-mentioned processes does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not be dealt with in the embodiments of the present application. implementation constitutes any limitation.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, for example, multiple units or components may be combined or integrated. to another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

Units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited to this. should be covered within the scope of protection of this application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

A data management method, comprising:

controlling storage of first reference data to the volatile memory, the first reference data including target data related to the first event;

Control stores the target data related to the first event from the volatile memory to non-volatile memory.
The method of claim 1 , said controlling to store said target data related to the first event from said volatile memory to non-volatile memory, comprising:

sending indication information to the volatile memory, where the indication information is used to instruct to transfer the target data related to the first event to the non-volatile memory; or,

The target data related to the first event is acquired from the volatile memory, and the target data related to the first event is sent to the non-volatile memory.
The method according to claim 1 or 2, wherein the first reference data includes at least one of perception data, positioning data, map data and wireless communication technology data.
The method according to any one of claims 1 to 3, wherein the method further comprises:

A first reference result is obtained by using the event prediction model and the first reference data, where the first reference result is used to indicate the occurrence probability of the first event obtained based on the first reference data.
The method according to any one of claims 1 to 3, wherein the method further comprises:

Obtain a second reference result by using the first reference data and the auxiliary data, where the second reference result is used to indicate the occurrence probability of the first event obtained based on the first reference data and the auxiliary data, and the auxiliary data includes The scene category and/or historical event statistical data where the terminal is located, the scene category includes at least one of high-speed scene, urban scene, mountain scene and extreme weather scene, and the historical event statistical data includes at least one event that occurred in history and reference data when each of the at least one event occurs.
The method according to any one of claims 1 to 5, wherein:

The target data related to the first event corresponds to the type of the first event and predefined data information; wherein, the type of the first event includes a rear-end collision, an overtaking accident, a passing vehicle accident, a pedestrian accident, At least one of non-motor vehicle accident, vehicle failure and takeover accident, the data information includes data type and time interval, and the data type includes at least one of sensor data, terminal status data and terminal dynamics data, so The time interval is the time interval between the first time node and the second time node; wherein, the first time node is any one of the following: the time node before the first event occurs, the place where the first event occurs The time node in the first event and the time node after the occurrence of the first event, the second time node is any one of the following: the time node before the first event occurs, the time node after the occurrence of the first event The time node in the first event and the time node after the occurrence of the first event.
The method of claim 6, wherein:

The sensor data includes one or more of the following: the position of at least one object around the terminal, the velocity of the at least one object, the volume of the at least one object, meteorological data, lighting data, camera data, or radar data;

The terminal status data includes one or more of the following: the terminal's driving speed, driving acceleration, accelerator pedal opening, brake pedal opening, steering wheel angle, steering torque, or light status; and/ or,

The terminal dynamics data includes quality information and/or braking delay parameters of the terminal.
The method according to any one of claims 1 to 7, wherein the method further comprises:

determining that a second event occurs, the second event is different from the first event, and the first reference data further includes target data related to the second event;

controlling storage of the target data from the volatile memory related to the second event from the volatile memory into the non-volatile memory;

Wherein, the type of the second event includes at least one of a rear-end collision accident, an overtaking accident, a passing vehicle accident, a pedestrian accident, a non-motor vehicle accident, a vehicle failure and a takeover accident.
The method according to any one of claims 1 to 8, wherein the method further comprises:

A first process is performed on the target data related to the first event, and the first process includes at least one of locking, encryption, time stamping and compression.
The method according to any one of claims 1 to 9, wherein if the first event does not occur within a preset time period, the method further comprises:

A second process is performed on the target data associated with the first event, the second process including unlocking, decrypting and/or erasing.
A data management apparatus, comprising a processor for:

controlling storage of first reference data to the volatile memory, the first reference data including target data related to the first event;

Control stores the target data related to the first event from the volatile memory to non-volatile memory.
The apparatus of claim 11, wherein the processor is configured to:

sending indication information to the volatile memory, where the indication information is used to instruct to transfer the target data related to the first event to the non-volatile memory; or,

The target data related to the first event is acquired from the volatile memory, and the target data related to the first event is sent to the non-volatile memory.
The apparatus according to claim 11 or 12, wherein the first reference data includes at least one of perception data, positioning data, map data and wireless communication technology data.
The apparatus according to any one of claims 11 to 13, wherein the processor is further configured to:

A first reference result is obtained by using the event prediction model and the first reference data, where the first reference result is used to indicate the occurrence probability of the first event obtained based on the first reference data.
The apparatus according to any one of claims 11 to 13, wherein the processor is further configured to:

Obtain a second reference result by using the first reference data and the auxiliary data, where the second reference result is used to indicate the occurrence probability of the first event obtained based on the first reference data, and the auxiliary data includes the location where the terminal is located The scene category and/or historical event statistical data, the scene category includes at least one of high-speed scene, urban scene, mountain scene and extreme weather scene, and the historical event statistical data includes at least one historical event and occurrence Reference data at each event of the at least one event.
The device according to any one of claims 11 to 15, characterized in that:

The target data related to the first event corresponds to the type of the first event and predefined data information; wherein, the type of the first event includes a rear-end collision, an overtaking accident, a passing vehicle accident, a pedestrian accident, At least one of non-motor vehicle accident, vehicle failure and takeover accident, the data information includes data type and time interval, and the data type includes at least one of sensor data, terminal status data and terminal dynamics data, so The time interval is the time interval between the first time node and the second time node; wherein, the first time node is any one of the following: the time node before the first event occurs, the place where the first event occurs The time node in the first event and the time node after the occurrence of the first event, the second time node is any one of the following: the time node before the first event occurs, the time node after the occurrence of the first event The time node in the first event and the time node after the occurrence of the first event.
The apparatus of claim 16, wherein:

The sensor data includes one or more of the following: the position of at least one target around the terminal, the velocity of the at least one target, the volume of the at least one target, meteorological data, camera data, or radar data;

The terminal status data includes one or more of the following: the terminal's driving speed, driving acceleration, accelerator pedal opening, brake pedal opening, steering wheel angle, steering torque, or light status; and/or,

The terminal dynamics data includes quality information and/or braking delay parameters of the terminal.
The apparatus according to any one of claims 11 to 17, wherein the processor is further configured to:

determining that a second event occurs, the second event is different from the first event, and the first reference data further includes target data related to the second event;

controlling storage of the target data from the volatile memory related to the second event from the volatile memory into the non-volatile memory;

Wherein, the type of the second event includes at least one of a rear-end collision accident, an overtaking accident, a passing vehicle accident, a pedestrian accident, a non-motor vehicle accident, a vehicle failure and a takeover accident.
The apparatus according to any one of claims 11 to 18, wherein the processor is further configured to:

A first process is performed on the target data related to the first event, and the first process includes at least one of locking, encryption, time stamping and compression.
The apparatus according to any one of claims 11 to 19, wherein if the first event does not occur within a preset first time period, the processor is further configured to:

A second process is performed on the target data associated with the first event, the second process including unlocking, decrypting and/or erasing.
A data management device, characterized by comprising an interface circuit and the processor according to any one of claims 11 to 20, wherein the interface circuit is coupled with the processor.
The apparatus of claim 21, wherein the interface circuit is used to connect at least one of a non-volatile memory or a volatile memory.
A terminal device, characterized by comprising the device according to any one of claims 11 to 22.
The terminal device according to claim 23, wherein the terminal device is an intelligent transportation device or a robot.
A computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, wherein when the instructions are executed on a computer, the instructions are used to execute the method according to any one of claims 1 to 10. method.
A computer program product comprising instructions, characterized in that, when the instructions are executed on a computer or a processor, the computer or the processor is made to implement any one of claims 1 to 10. one of the methods described.