CN112307094B - Automatic driving data reading method and device, computer equipment and storage medium - Google Patents
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Abstract
At least one embodiment of the invention discloses an automatic driving data reading method, an automatic driving data reading device, computer equipment and a storage medium, wherein the method can comprise the following steps: acquiring a data reading request of a user, wherein the data reading request carries a designated time interval and a designated sensor type; screening out sensor data with time stamps within a specified time interval and belonging to a specified sensor type from the table storage system, and returning the sensor data to a user; the table storage system stores sensor triplet data obtained after splitting the automatic driving data, wherein the sensor triplet data consists of a time stamp, a sensor type and sensor data. The scheme shown in at least one embodiment of the invention can improve the efficiency of data reading and the like.
Description
[ field of technology ]
The embodiment of the invention relates to the technical field of automatic driving, in particular to an automatic driving data reading method, an automatic driving data reading device, computer equipment and a storage medium.
[ background Art ]
In recent years, automated driving (Autonomous driving) systems have been widely used. The general-purpose autopilot system includes various sensors, such as a forward camera (camera_front), a point cloud (point_closed), and the like.
With the increasing duration and mileage of autonomous vehicles, the above sensors generate a large amount of data. For these generated data, the following storage methods are generally used at present: the data of the sensors are fused together in an unstructured mode and stored in a file, the file storing the data is usually segmented according to the dimensions of time length, mileage or file size, for example, data generated per minute are segmented into one file in a unit of minutes, for example, 200GB data are segmented into one file in a unit of 200GB data, for example, 10KM mileage is used, data generated per 10KM mileage are segmented into one file, and the like, and in addition, the data segmentation can be performed in a mode of combining the three dimensions.
When a user needs to access stored data, for example, a specific 10 seconds of data of a forward facing camera needs to be read, it needs to be processed as follows: assuming that the 10 seconds of data is 500MB in total, first, file data containing the 10 seconds of data is pulled and downloaded from a remote end where the sensor data is stored; assuming that the single file size in the remotely stored data is 10GB, it is also necessary to screen out the 10GB data for the specific 10 seconds required; once the 10 second data is distributed among the two files, it is necessary to pull and download the two files and screen out the specific 10 second data required from the 20GB data. It can be seen that most of the downloaded data is pulled to be unnecessary data, thereby affecting the efficiency of data reading.
[ invention ]
In view of this, the present invention provides an automatic driving data reading method, apparatus, computer device, and storage medium.
The specific technical scheme is as follows:
an automatic driving data reading method, comprising:
acquiring a data reading request of a user, wherein the data reading request carries a designated time interval and a designated sensor type;
screening out sensor data with time stamps within the specified time interval and belonging to the specified sensor type from a table storage system, and returning the sensor data to the user; the table storage system stores sensor triplet data obtained by splitting automatic driving data, wherein the sensor triplet data comprises a time stamp, a sensor type and sensor data.
An automatic driving data reading device comprising: a request acquisition unit and a data screening unit;
the request acquisition unit is used for acquiring a data reading request of a user, wherein the data reading request carries a designated time interval and a designated sensor type;
the data screening unit is used for screening out sensor data, the time stamp of which is positioned in the appointed time interval and belongs to the appointed sensor type, from the table storage system and returning the sensor data to the user; the table storage system stores sensor triplet data obtained by splitting automatic driving data, wherein the sensor triplet data comprises a time stamp, a sensor type and sensor data.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a method as described above when executing the program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as described above.
A computer program product comprising a computer program which, when executed by a processor, implements a method as described above.
Based on the above description, according to the scheme of the invention, the automatic driving data can be split, so that the sensor triplet data composed of the time stamp, the sensor type and the sensor data is obtained and stored in the table storage system, and therefore, when the data is required to be read, the required data can be screened out from the table storage system directly based on the time stamp, the sensor type and the like, such as the specific 10 second data of the front camera, and the data reading efficiency and the like are improved compared with the existing mode.
[ description of the drawings ]
Fig. 1 is a flowchart of an embodiment of an automatic driving data reading method according to the present invention.
FIG. 2 is a schematic diagram of an automatic driving data storage and reading process according to the present invention.
Fig. 3 is a schematic structural diagram of an embodiment of an automatic driving data reading device according to the present invention.
Fig. 4 illustrates a block diagram of an exemplary computer system/server 12 suitable for use in implementing embodiments of the present invention.
[ detailed description ] of the invention
In order to make the technical solution of the present invention more clear and obvious, the solution of the present invention will be further described below by referring to the accompanying drawings and examples.
It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
In addition, it should be understood that the term "and/or" herein is merely one association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Fig. 1 is a flowchart of an embodiment of an automatic driving data reading method according to the present invention. As shown in fig. 1, the following detailed implementation is included.
In 101, a data reading request of a user is acquired, where the data reading request carries a specified time interval and a specified sensor type.
In 102, sensor data with time stamps within a specified time interval and belonging to a specified sensor type is screened out from a table storage system and returned to a user; the table storage system stores sensor triplet data obtained after splitting the automatic driving data, wherein the sensor triplet data consists of a time stamp, a sensor type and sensor data.
The autopilot data is various sensor data generated during the running process of the autopilot vehicle, such as camera_front, short-focus camera (camera_short), point_closed, inertial measurement unit (imu), radar (radar) and the like.
In order to support the data reading, the automatic driving data needs to be split and stored in advance. Specifically, autopilot data may be obtained and split, at least one sensor triplet data extracted, and the at least one sensor triplet data stored as relational data in a table storage system.
In practical applications, to increase the processing speed, multiple processes (or threads) may be started at the same time, and the data splitting process may be performed in parallel.
And the data splitting can be carried out by taking the time stamp, the sensor type and the sensor data triples as granularity, so that each piece of sensor triples data consisting of the time stamp, the sensor type and the sensor data is obtained. For example, a sensor triplet is as follows: 1555902564001+camera_front+front_png, wherein 1555902564001 is a time stamp, camera_front is a sensor type, and front_png is sensor data.
The split sensor triplet data may be stored in a table storage system in a predetermined format. Preferably, two storage modes are provided in this embodiment, and for convenience of description, they are referred to as a first storage mode and a second storage mode, respectively, and are described below.
First) first storage mode
In this manner, each sensor triplet data may be stored as a record in the table storage system.
Each record may include the following in sequence: time stamp, sensor type, and sensor data. Further, each record may be stored in the order of the time stamps included from far to near.
The following table is a schematic diagram of the first storage mode according to the present invention:
| timestamp | sensor_type | sensor_data | driving_id |
| 1555902564001 | camera_front | front_png | 1 |
| 1555902564002 | camera_front | front_png | 1 |
| 1555902564002 | point_cloud | xxx.pcd | 1 |
| 1555902564003 | imu | imu_data | 1 |
| 1555902564003 | camera_front | front_png | 1 |
| 1555902564003 | point_cloud | xxx.pcd | 1 |
| …… | …… | …… | …… |
table one first storage mode
As shown in table one, the table at least needs to include three columns, wherein the first column is a timestamp (timestamp), the second column is a sensor type (sensor_type), and the third column is sensor data (sensor_data).
The time stamp may be accurate to milliseconds. The sensor data are sensor data corresponding to different sensor types obtained through analysis, for example, the sensor data corresponding to camera_front can be png picture data, and the sensor data corresponding to point_closed can be point cloud data in a pcd format and the like.
"1555902564001+camera_front+front_png" shown in Table one constitutes a record, and similarly, "1555902564002+point_closed+xxx.pcd" constitutes a record. The records may be stored sequentially in the order of far to near time stamps included. Wherein, for two or more sensor triple data with the same time stamp, the sequence can be set at will.
As shown in table one, each record may further include the following: the driving_id (driving_id), i.e. table one, may further comprise a fourth column. The driving_id is mainly used for a scene that multiple vehicles run simultaneously, and if two autonomous vehicles run simultaneously, the acquired autonomous data comprise data from the two vehicles, and in order to distinguish the sources of the data, the driving_id can be used for identifying, for example, 1 can be used for identifying one autonomous vehicle, and 2 can be used for identifying the other autonomous vehicle. The above is mainly aimed at the case of storing multiple vehicles together, if the data of different vehicles are stored separately, the driving_id may not be used to distinguish the sources of the data, or the driving_id may be reserved, where the values of the driving_id in each record are the same, for example, all the values are 1.
In addition to the above, each record may further include some other contents, that is, other columns may be further introduced in table one, and the specific contents may be determined according to actual needs.
Based on the above storage mode, the following data reading mode may be adopted: acquiring a data reading request, wherein the data reading request carries a designated time interval (data_time_range) and a designated sensor type; sensor data having a timestamp within a specified time interval and belonging to a specified sensor type is screened from the table storage system.
For example, the specified time interval is: [1555902564001, 1555902564003], the specified sensor types are: the camera_front and point_closed can screen the sensor data with the time stamp in the time interval of [1555902564001, 1555902564003] from the table storage system, and the sensor type is camera_front or point_closed, and the sensor data is returned as a screening result.
Two) second storage mode
In this embodiment, the following processes may be performed for each of the different time stamps obtained by splitting: and counting all sensor triplet data comprising the time stamp, and fusing the sensor triplet data obtained through counting to obtain a record which is stored in a table storage system.
Each record may include the following in sequence: timestamp and first to nth data segments. Wherein N is a positive integer, and the value is equal to the total number of the sensor types. Each data segment corresponds to one sensor type, if the sensor data of the sensor type corresponding to the data segment exists in the sensor triplet data obtained through statistics, the sensor data of the corresponding sensor type can be stored in the data segment, otherwise, the data segment can be empty. Further, each record may be stored in the order of the time stamps included from far to near.
The following table is a schematic diagram of the second storage mode according to the present invention:
second storage mode of table
As shown in Table II, the table at least needs to include N+1 columns, wherein the first column is a timestamp, the other columns respectively correspond to different sensor types, such as the second column (sensor_1) corresponds to camera_front, the third column (sensor_2) corresponds to point_closed, and the N+1 column (sensor_N) corresponds to imu.
As shown in table two, for the time stamp 1555902564001, assuming that there is only one piece of sensor triplet data containing the time stamp, namely 1555902564001+camera_front+front_png, the camera_front+front_png in the sensor triplet data can be stored into the corresponding data segment in the record where the time stamp is located. For the time stamp 1555902564002, assuming that there are two pieces of sensor triplet data containing the time stamp, the camera_front+front_png and point_closed+xxx.pcd in the two pieces of sensor triplet data may be stored into the corresponding data segment in the record where the time stamp is located, respectively. The other parts will not be described in detail.
As shown in table two, each record may further include the following: the driving_id, table two, may further include column n+2. The driving_id is mainly used for a scene that multiple vehicles run simultaneously, and if two autonomous vehicles run simultaneously, the acquired autonomous data comprise data from the two vehicles, and in order to distinguish the sources of the data, the driving_id can be used for identifying, for example, 1 can be used for identifying one autonomous vehicle, and 2 can be used for identifying the other autonomous vehicle. The above is mainly aimed at the case of storing multiple vehicles together, if the data of different vehicles are stored separately, the driving_id may not be used to distinguish the sources of the data, or the driving_id may be reserved, where the values of the driving_id in each record are the same, for example, all the values are 1.
In addition to the above, each record may further include some other contents, that is, other columns may be further introduced in table two, and the specific contents may be determined according to actual needs.
Based on the above storage mode, the following data reading mode may be adopted: acquiring a data reading request, wherein the data reading request carries a designated time interval and a designated sensor type; sensor data having a timestamp within a specified time interval and belonging to a specified sensor type is screened from the table storage system.
For example, the specified time interval is: [1555902564001, 1555902564003], the specified sensor types are: the camera_front and point_closed can screen the sensor data with the time stamp in the time interval of [1555902564001, 1555902564003] from the table storage system, and the sensor type is camera_front or point_closed, and the sensor data is returned as a screening result.
In practical applications, the specific use of the first storage mode or the second storage mode may be determined according to practical needs.
In view of the foregoing description, fig. 2 is a schematic diagram of an automatic driving data storage and reading process according to the present invention, and the detailed implementation is referred to the foregoing related description and will not be repeated.
It should be noted that, for the sake of simplicity of description, the foregoing method embodiments are expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present invention is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
In summary, after the scheme of the embodiment of the method is adopted, after the automatic driving data is acquired, the automatic driving data can be split first, so that sensor triplet data formed by time stamps, sensor types and sensor data are obtained, the split sensor triplet data can be stored in a table storage system according to a first storage mode or a second storage mode, and therefore, when the data is required to be read, the required data can be screened out from the table storage system directly based on the time stamps, the sensor types and the like, and the efficiency of the data reading is improved compared with the prior mode.
The above description of the method embodiments further describes the solution of the present invention by means of device embodiments.
Fig. 3 is a schematic structural diagram of an embodiment of an automatic driving data reading device according to the present invention. As shown in fig. 3, includes: a request acquisition unit 301 and a data screening unit 302.
The request acquiring unit 301 is configured to acquire a data reading request of a user, where the data reading request carries a specified time interval and a specified sensor type.
A data screening unit 302, configured to screen out sensor data whose time stamp is located in a specified time interval and belongs to a specified sensor type from the table storage system, and return the sensor data to the user; the table storage system stores sensor triplet data obtained after splitting the automatic driving data, wherein the sensor triplet data consists of a time stamp, a sensor type and sensor data.
Accordingly, the apparatus shown in fig. 3 may further include: the splitting storage unit 300 is configured to obtain autopilot data, split autopilot data, extract at least one sensor triplet data, and store the at least one sensor triplet data as relational data in the table storage system.
The autopilot data is various sensor data generated in the running process of the autopilot vehicle, such as various sensor data of camera_front, camera_short, point_closed, imu, radar and the like.
For the acquired autopilot data, the split storage unit 300 may perform a split process thereon. In practical applications, to increase the processing speed, the split storage unit 300 may simultaneously start a plurality of processes (or threads) to perform the data splitting process in parallel.
The split storage unit 300 may split data with the time stamp, the sensor type, and the sensor data triplet as granularity, thereby obtaining each piece of sensor triplet data composed of the time stamp, the sensor type, and the sensor data.
Further, the split storage unit 300 may store the split sensor triplet data in a predetermined format into a table storage system. Preferably, two storage modes are provided in this embodiment, and for convenience of description, they will be referred to as a first storage mode and a second storage mode, respectively.
In the first storage manner, the split storage unit 300 may store each sensor triplet data as one record in the table storage system.
Each record may include the following in sequence: time stamp, sensor type, and sensor data. In addition, the split storage unit 300 may store the records in the order from far to near, respectively, the time stamps included.
In the second storage manner, the splitting storage unit 300 may perform the following processes for each of the different time stamps obtained by splitting: and counting all sensor triplet data comprising the time stamp, fusing the sensor triplet data obtained by counting to obtain a record, and storing the record in a table storage system.
Each record may include the following in sequence: timestamp and first to nth data segments. Wherein N is a positive integer, and the value is equal to the total number of the sensor types. Each data segment corresponds to one sensor type, if the sensor data of the sensor type corresponding to the data segment exists in the sensor triplet data obtained through statistics, the sensor data of the corresponding sensor type can be stored in the data segment, otherwise, the data segment can be empty. In addition, the split storage unit 300 may store the records in the order from far to near, respectively, the time stamps included.
Based on the above storage manner, after the request obtaining unit 301 obtains the data reading request of the user, the data filtering unit 302 may screen out the sensor data whose time stamp is in the time interval carried in the data reading request and belongs to the sensor type carried in the data reading request from the table storage system, and return the sensor data to the user.
The specific workflow of the embodiment of the apparatus shown in fig. 3 is referred to the related description in the foregoing method embodiment, and will not be repeated.
In summary, by adopting the scheme of the embodiment of the invention, after the automatic driving data is acquired, the automatic driving data can be split firstly, so that each piece of sensor triplet data consisting of the timestamp, the sensor type and the sensor data is obtained, and the split obtained sensor triplet data can be stored in the table storage system according to the first storage mode or the second storage mode, so that when the data is required to be read, the required data can be screened out from the table storage system directly based on the timestamp, the sensor type and the like, and the efficiency of the data reading is improved compared with the prior mode.
Fig. 4 illustrates a block diagram of an exemplary computer system/server 12 suitable for use in implementing embodiments of the present invention. The computer system/server 12 shown in FIG. 4 is intended as an example, and should not be taken as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 4, the computer system/server 12 is in the form of a general purpose computing device. Components of computer system/server 12 may include, but are not limited to: one or more processors (processing units) 16, a memory 28, a bus 18 that connects the various system components, including the memory 28 and the processor 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer system/server 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer system/server 12 and includes both volatile and non-volatile media, removable and non-removable media.
Memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The computer system/server 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard disk drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The computer system/server 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer system/server 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer system/server 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, the computer system/server 12 can communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 20. As shown in fig. 4, the network adapter 20 communicates with other modules of the computer system/server 12 via the bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with computer system/server 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processor 16 executes various functional applications and data processing, such as the method of the embodiment shown in fig. 1, by running programs stored in the memory 28.
The invention also discloses a computer-readable storage medium on which a computer program is stored which, when being executed by a processor, will carry out the method according to the embodiment shown in fig. 1.
Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus and method, etc. may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is merely a logical function division, and there may be other manners of division when actually implemented.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform part of the steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather to enable any modification, equivalent replacement, improvement or the like to be made within the spirit and principles of the invention.
Claims (12)
1. An automatic driving data reading method, characterized by comprising:
acquiring a data reading request of a user, wherein the data reading request carries a designated time interval and a designated sensor type;
screening out sensor data with time stamps within the specified time interval and belonging to the specified sensor type from a table storage system, and returning the sensor data to the user; the table storage system stores sensor triplet data obtained by splitting automatic driving data, wherein the sensor triplet data consists of a time stamp, a sensor type and sensor data;
wherein storing the split sensor triplet data into the table storage system comprises: respectively counting all sensor triplet data containing the time stamp for each different time stamp obtained by splitting, fusing the sensor triplet data obtained by counting to obtain a record, and storing the record into the table storage system; each record contains the following contents: the method comprises the steps of time stamping, N data segments and an automatic driving running identifier, wherein N is a positive integer, and the value is equal to the total number of sensor types; each data segment corresponds to a sensor type, if the sensor data of the sensor type corresponding to the data segment exists in the sensor triplet data obtained through statistics, the sensor data of the corresponding sensor type is stored in the data segment, otherwise, the data segment is empty, the automatic driving running identifier is used for distinguishing the source of the data in the corresponding record, and the source comprises different automatic driving vehicles.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
before the data reading request of the user is acquired, the method further comprises the following steps:
acquiring automatic driving data;
splitting the automatic driving data, and extracting at least one sensor triplet data;
the at least one sensor triplet data is stored as relational data in a table storage system.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the storing the split sensor triplet data in the table storage system further includes: and respectively storing each sensor triplet data as a record into the table storage system.
4. The method of claim 3, wherein the step of,
each record contains the following contents: timestamp, sensor type, and sensor data;
the method further comprises the steps of: the records are stored in the order from far to near according to the contained time stamps.
5. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the method further comprises the steps of: the records are stored in the order from far to near according to the contained time stamps.
6. An automatic driving data reading device, characterized by comprising: the system comprises a request acquisition unit, a data screening unit and a split storage unit;
the request acquisition unit is used for acquiring a data reading request of a user, wherein the data reading request carries a designated time interval and a designated sensor type;
the data screening unit is used for screening out sensor data, the time stamp of which is positioned in the appointed time interval and belongs to the appointed sensor type, from the table storage system and returning the sensor data to the user; the table storage system stores sensor triplet data obtained by splitting automatic driving data, wherein the sensor triplet data consists of a time stamp, a sensor type and sensor data;
the splitting storage unit is used for respectively counting all sensor triplet data containing the time stamps for each different time stamp obtained by splitting, fusing the sensor triplet data obtained by counting to obtain a record, and storing the record into the table storage system; each record contains the following contents: the method comprises the steps of time stamping, N data segments and an automatic driving running identifier, wherein N is a positive integer, and the value is equal to the total number of sensor types; each data segment corresponds to a sensor type, if the sensor data of the sensor type corresponding to the data segment exists in the sensor triplet data obtained through statistics, the sensor data of the corresponding sensor type is stored in the data segment, otherwise, the data segment is empty, the automatic driving running identifier is used for distinguishing the source of the data in the corresponding record, and the source comprises different automatic driving vehicles.
7. The apparatus of claim 6, wherein the device comprises a plurality of sensors,
the splitting storage unit is further used for acquiring automatic driving data, splitting the automatic driving data, extracting at least one sensor triplet data, and storing the at least one sensor triplet data as relational data in a table storage system.
8. The apparatus of claim 6, wherein the device comprises a plurality of sensors,
the splitting storage unit is further used for respectively storing each sensor triplet data into the table storage system as one record.
9. The apparatus of claim 8, wherein the device comprises a plurality of sensors,
each record contains the following contents: timestamp, sensor type, and sensor data;
the splitting storage unit is further used for storing each record according to the sequence from far to near of the included time stamps.
10. The apparatus of claim 6, wherein the device comprises a plurality of sensors,
the splitting storage unit is further used for storing each record according to the sequence from far to near of the included time stamps.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when the program is executed.
12. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1-5.
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