CN117909165A

CN117909165A - Data acquisition method and device, electronic equipment and storage medium

Info

Publication number: CN117909165A
Application number: CN202311764587.9A
Authority: CN
Inventors: 赵兰兰
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2023-12-20
Filing date: 2023-12-20
Publication date: 2024-04-19

Abstract

The disclosure relates to the technical field of automatic driving, and in particular relates to a data acquisition method, a data acquisition device, electronic equipment and a storage medium. The specific implementation scheme is as follows: acquiring a pre-configured data acquisition rule; responding to a data acquisition instruction, creating a data acquisition task and associating a data acquisition rule; generating a corresponding task list according to the data acquisition rule and at least one data acquisition task; and sending the task list to the vehicle end. The user can define and issue data acquisition rules through the cloud, agree on the form of data acquisition boundaries, achieve accurate data acquisition, reduce the return cost of invalid data, and improve the utilization rate of data.

Description

Data acquisition method and device, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of automatic driving, and in particular relates to a data acquisition method, a data acquisition device, electronic equipment and a storage medium.

Background

In the development of autopilot algorithms, a large amount of autopilot data needs to be collected. However, due to the diversity of data application scenarios, the requirements for data in different research and development scenarios are different, including but not limited to time, location, vehicles, etc. The existing technical scheme is that data acquisition is completed through a full-volume disc-falling mode or a cloud end real-time return mode, massive disc-falling data are stored in a data platform, and a user retrieves and acquires required data through a tag. This results in a large amount of data waste that gathers, and after the data effectiveness, the data conversion rate is lower, causes to gather the wasting of resources.

Disclosure of Invention

The disclosure provides a data acquisition method, a data acquisition device, electronic equipment and a storage medium.

According to a first aspect of the present disclosure, there is provided a data acquisition method, comprising:

Acquiring a pre-configured data acquisition rule;

responding to a data acquisition instruction, creating a data acquisition task and associating the data acquisition rule;

Generating a corresponding task list according to the data acquisition rule and at least one data acquisition task; the task list is used for indicating a vehicle end to collect data in the running process of the vehicle;

and sending the task list to the vehicle end.

According to a second aspect of the present disclosure, there is provided a data acquisition method comprising:

Receiving a task list issued by the cloud according to a data acquisition rule; wherein the task list includes at least one data acquisition task;

Executing the corresponding data acquisition task according to the task list;

and transmitting the acquired automatic driving data back to the cloud.

According to a third aspect of the present disclosure, there is provided a data acquisition device comprising:

the acquisition module is configured to acquire a pre-configured data acquisition rule;

The task creation module is configured to respond to the data acquisition instruction, create a data acquisition task and associate the data acquisition rule;

The task list generation module is configured to generate a corresponding task list according to the data acquisition rule and at least one data acquisition task; the task list is used for indicating a vehicle end to collect data in the running process of the vehicle;

And the task issuing module is configured to send the task list to the vehicle end.

According to a fourth aspect of the present disclosure, there is provided a data acquisition device comprising:

The task receiving module is configured to receive a task list issued by the cloud according to the data acquisition rule; wherein the task list includes at least one data acquisition task;

The task execution module is configured to execute the corresponding data acquisition task according to the task list;

and the data returning module is configured to return the acquired automatic driving data to the cloud.

According to a fifth aspect of the present disclosure, there is provided an electronic device comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the above claims.

According to a sixth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of the above-mentioned technical solutions.

According to a seventh aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of any of the above technical solutions.

According to an eighth aspect of the present disclosure, there is provided an autonomous vehicle comprising the electronic device described in the above technical solution.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

Fig. 1 is a schematic diagram illustrating steps of a data acquisition method applied to a cloud in an embodiment of the disclosure;

FIG. 2 is a task issuing flow chart of a data acquisition method in an embodiment of the present disclosure;

Fig. 3 is a schematic step diagram of a data acquisition method applied to a vehicle end in an embodiment of the disclosure;

FIG. 4 is a graph of data acquisition tasks versus data acquisition rules in an embodiment of the present disclosure;

FIG. 5 is a functional block diagram of a data acquisition device applied to a cloud in an embodiment of the present disclosure;

FIG. 6 is a functional block diagram of a data acquisition device applied to a vehicle end in an embodiment of the present disclosure;

Fig. 7 is a block diagram of an electronic device for implementing a data acquisition method of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the prior art, data acquisition is completed through a full-volume disc-falling or vehicle-end cloud-end real-time feedback mode, massive disc-falling data are stored in a data platform (disc-falling is that data are written into a magnetic disc), and a user retrieves and acquires the required data through a tag. Full-scale landing of mass data this can result in a large volume of data, the cost of the disk space is high, the occupation of data processing resources is great. Besides the data required by the user, other large amounts of data may not be used, the data conversion rate meeting the user requirements is not high, and the effectiveness is affected by objective environment. In addition, the scheme of real-time return of the vehicle end and the cloud end has the advantages that the data volume of single return is limited, the flow cost is extremely high, and the data quality of return cannot be guaranteed.

To solve the above technical problems in the prior art, in order to improve the utilization rate of collected data and reduce the data collection cost, the present disclosure provides a data collection method, which may be applied to a cloud, as shown in fig. 1, and includes:

step S101, a preconfigured data acquisition rule is obtained.

Step S102, responding to the data acquisition instruction, creating a data acquisition task and associating the data acquisition rule.

Step S103, generating a corresponding task list according to the data acquisition rule and at least one data acquisition task; the task list is used for indicating the vehicle end to collect data in the running process of the vehicle.

Step S104, a task list is sent to the vehicle end.

Specifically, taking automatic driving data collection as an example, automatic driving data collection refers to collecting data such as the surrounding environment, the state and the driving behavior of a vehicle through devices such as sensors, cameras and radars carried by the vehicle. Such data may include images, video, lidar scans, inertial Measurement Unit (IMU) data, GPS location information, and the like. The data collection rule is used for defining the collected data type, the collected data vehicle terminal model, the data collection priority and the like, and a corresponding vehicle end algorithm version can be defined, for example, the data collection rule is added for a traffic light identification algorithm so as to meet corresponding functional requirements.

Through the technical scheme, the user can define and issue data acquisition rules through the cloud, agree on the form of the data acquisition boundary, and achieve accurate data acquisition. In short, what data is needed to be landed, and unnecessary data is not landed any more, so that the return cost of invalid data is further reduced, and the utilization rate of the data is improved.

As an optional embodiment, step S101, before obtaining the preconfigured data collection rule, further includes:

Configuring data acquisition rules at a cloud; or alternatively

And setting a corresponding data acquisition model at the vehicle end according to the data acquisition rule.

Specifically, the data acquisition rules can be configured at the cloud and issued to the vehicle end, or can be configured in an algorithm shadow mode at the vehicle end, namely, the vehicle end is provided with the data acquisition model, and the vehicle end directly acquires the corresponding data acquisition rules through the data acquisition model, so that the cloud is not required to issue the data acquisition rules, and the aim of accurate data dynamic disc placement is fulfilled. Under the condition that the vehicle end configures the data acquisition model, if the data acquisition rule needs to be modified, the new data acquisition rule can be issued by the cloud end for updating.

And configuring the effective conditions of the data acquisition rules.

Specifically, the user (e.g., a vehicle manufacturer developer) can be allowed to configure the validation condition corresponding to the data acquisition rule, for example, the validation is performed when the vehicle-end algorithm in the data acquisition rule is triggered, or the validation is performed in a specific city range, so that the data required by the user can be acquired more accurately.

As an optional implementation manner, step S101, after obtaining the preconfigured data collection rule, further includes:

And carrying out hardware configuration on the vehicle according to the data acquisition rule.

Specifically, the hardware configuration of the vehicle includes: the acquisition parameters of the sensors of the vehicle are configured. The vehicle manufacturer developer is allowed to configure associated data hardware (e.g., specific sensors on the vehicle) in the data collection rules, e.g., defining the use of the image sensor on the left side of the vehicle to collect image data of the traffic light ahead.

As an optional embodiment, step S103, generating the corresponding task list according to the data acquisition rule and the at least one data acquisition task includes:

and determining the priorities of the plurality of data acquisition tasks according to the data acquisition rules in response to the number of the data acquisition tasks being a plurality of.

Specifically, as shown in fig. 2, if the cloud end 100 needs to issue a plurality of data acquisition tasks to the vehicle end 200, before issuing a plurality of data acquisition tasks to the vehicle, the priority of the data acquisition tasks can be determined according to the importance degree of the data to be acquired, so as to ensure that the data acquisition tasks with higher priority are executed preferentially.

As an optional implementation manner, step S104 further includes, after sending the task list to the vehicle end:

And responding to the vehicle end to execute the corresponding data acquisition task according to the task list, and receiving the automatic driving data acquired by the vehicle end.

Specifically, when the cloud end receives the data backhaul from the vehicle end in the embodiment, after the data is parsed, the data may be filtered, and part of the unnecessary data is deleted and is not stored in the data platform of the cloud end.

and monitoring the data acquisition task and determining the triggering state of the data acquisition task.

As shown in fig. 2, after the cloud 100 generates a corresponding task list according to one or more data acquisition tasks and issues the task list to the vehicle end 200, the triggering state of each data acquisition task may be monitored, so as to ensure that the data acquisition task is successfully triggered. For example, the data volume of the collected data returned by the vehicle end can be monitored, and if 100M of data needs to be returned, but if the data received by the cloud end is 0M, the data collection task is judged to be not triggered successfully; if the data received by the cloud is 99M, judging that the data acquisition task is successfully triggered and is currently being executed; if the data received by the cloud is 100M, the data acquisition task is judged to be successfully triggered, and the task is executed.

The disclosure further provides a data acquisition method, which may be applied to a vehicle end, as shown in fig. 3, including:

step S301, a task list issued by a cloud according to a data acquisition rule is received; wherein the task list includes at least one data acquisition task.

Step S302, corresponding data acquisition tasks are executed according to the task list.

Step S303, the acquired automatic driving data is transmitted back to the cloud.

Specifically, the data collection rule refers to a data type, a vehicle terminal model, a data collection priority and the like of collected data, and a corresponding vehicle end algorithm version can be defined, for example, the data collection rule is added for a traffic light identification algorithm so as to meet corresponding functional requirements. As shown in fig. 4 as a graph of the relationship between data collection tasks and data collection rules, during the rule configuration process, the collected data content, the data collection hardware (e.g., which sensor of the vehicle is used to collect a particular data content), and the validation conditions for the data collection rules (e.g., the data collection rules are validated within a particular city) may be configured.

As an optional implementation manner, step S301, after receiving the task list issued by the cloud according to the data collection rule, further includes:

Comparing the data acquisition rule with the local acquisition rule:

Responding to the local acquisition rule containing the data acquisition rule, and executing a data acquisition task according to the data acquisition rule;

and responding to the fact that the local acquisition rule does not contain the data acquisition rule, updating the local acquisition rule according to the data acquisition rule, and executing a data acquisition task according to the updated local acquisition rule.

Specifically, before executing the data acquisition task issued by the cloud, the embodiment may first match a local acquisition rule at the vehicle end, determine whether the rule exists locally, and if so, directly execute the data acquisition task. If the data acquisition rule issued by the cloud is not available, the data acquisition rule issued by the cloud can be added to the local acquisition rule, and then the corresponding data acquisition task is executed, so that the data acquisition task can be executed.

The present disclosure also provides a data acquisition device 500, as shown in fig. 5, comprising:

An acquisition module 501 configured to acquire pre-configured data acquisition rules.

The task creation module 502 is configured to create a data collection task in response to a data collection instruction and associate a data collection rule.

A task list generation module 503 configured to generate a corresponding task list according to the data acquisition rule and the at least one data acquisition task; the task list is used for indicating the vehicle end to collect data in the running process of the vehicle.

The task issuing module 504 is configured to send the task list to the vehicle end.

As an alternative embodiment, the data acquisition device 500 further includes:

the first configuration module is configured to configure the data acquisition rule at the cloud before the acquisition module acquires the pre-configured data acquisition rule; or alternatively

The first configuration module sets a corresponding data acquisition model at the vehicle end according to the data acquisition rule.

As an alternative embodiment, the data acquisition device 500 further includes:

the second configuration module is configured to configure the validation condition of the data acquisition rule before the acquisition module 501 acquires the pre-configured data acquisition rule.

As an alternative embodiment, the data acquisition device 500 further includes:

and the third configuration module is configured to perform hardware configuration on the vehicle according to the data acquisition rule after the acquisition module acquires the pre-configured data acquisition rule.

As an alternative embodiment, the task list generating module 503 generates a corresponding task list according to the data collection rule and at least one data collection task, including:

As an alternative embodiment, the data acquisition device 500 further includes:

The data receiving module is configured to receive the automatic driving data collected by the vehicle end in response to the vehicle end executing the corresponding data collection task according to the task list after the task issuing module 504 sends the task list to the vehicle end.

As an alternative embodiment, the data acquisition device 500 further includes:

The monitoring module is configured to monitor the data acquisition task in the task list after the task issuing module 504 sends the task list to the vehicle end, and determine the triggering state of the data acquisition task.

The present disclosure also provides a data acquisition device 600, as shown in fig. 6, comprising:

The task receiving module 601 is configured to receive a task list issued by the cloud according to a data acquisition rule; wherein the task list includes at least one data acquisition task.

The task execution module 602 is configured to execute a corresponding data acquisition task according to the task list.

The data feedback module 603 is configured to feedback the collected autopilot data to the cloud.

As an alternative embodiment, the data acquisition device 600 further includes:

The rule comparison module is configured to compare the data acquisition rule with the local acquisition rule after the task receiving module receives the task list issued by the cloud according to the data acquisition rule:

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

Fig. 7 illustrates a schematic block diagram of an example electronic device 700 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 7, the apparatus 700 includes a computing unit 701 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data required for the operation of the device 700 may also be stored. The computing unit 701, the ROM 702, and the RAM703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in device 700 are connected to I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, etc.; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, an optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning objective function algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as a data acquisition method. For example, in some embodiments, the data collection method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 700 via ROM 702 and/or communication unit 709. When a computer program is loaded into RAM703 and executed by computing unit 701, one or more steps of the data acquisition method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the data acquisition method by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A data acquisition method comprising:

Acquiring a pre-configured data acquisition rule;

and sending the task list to the vehicle end.

2. The method of claim 1, wherein prior to the obtaining the pre-configured data collection rules, further comprising:

Configuring the data acquisition rule at the cloud; or alternatively

3. The method according to claim 1 or 2, wherein prior to the acquiring the pre-configured data acquisition rules, further comprising:

And configuring the effective conditions of the data acquisition rules.

4. A method according to any one of claims 1-3, wherein after said obtaining the pre-configured data acquisition rules, further comprising:

5. The method of claim 4, wherein the hardware configuration of the vehicle according to the data collection rules comprises:

and configuring acquisition parameters of sensors of the vehicle.

6. The method of claim 1, wherein the generating a corresponding task list according to the data collection rules and at least one of the data collection tasks comprises:

And determining priorities of a plurality of data acquisition tasks according to the data acquisition rules in response to the number of the data acquisition tasks being a plurality of.

7. The method of claim 1, wherein after the sending the task list to the vehicle end, further comprising:

And monitoring the data acquisition task in the task list, and determining the triggering state of the data acquisition task.

8. The method according to any one of claims 1-7, wherein after the sending the task list to the vehicle end, further comprises:

9. A data acquisition method comprising:

Executing the corresponding data acquisition task according to the task list;

and transmitting the acquired automatic driving data back to the cloud.

10. The method of claim 9, wherein after the receiving the task list issued by the cloud according to the data collection rule, further comprises:

Comparing the data acquisition rule with a local acquisition rule:

Responding to the local acquisition rule containing the data acquisition rule, and executing the data acquisition task according to the data acquisition rule;

and responding to the fact that the local acquisition rule does not contain the data acquisition rule, updating the local acquisition rule according to the data acquisition rule, and executing the data acquisition task according to the updated local acquisition rule.

11. A data acquisition device, comprising:

12. The apparatus of claim 11, further comprising:

13. The apparatus of claim 11 or 12, further comprising:

The second configuration module is configured to configure the effective condition of the data acquisition rule before the acquisition module acquires the pre-configured data acquisition rule.

14. The apparatus of any of claims 11-13, further comprising:

15. The apparatus of claim 14, wherein the third configuration module hardware configures the vehicle according to the data collection rules comprises:

and configuring acquisition parameters of sensors of the vehicle.

16. The apparatus of claim 11, wherein the task list generation module generates a corresponding task list according to the data acquisition rule and at least one of the data acquisition tasks comprises:

17. The apparatus of claim 11, further comprising:

The monitoring module is configured to monitor the data acquisition task in the task list after the task issuing module sends the task list to the vehicle end, and determine the triggering state of the data acquisition task.

18. The apparatus of any of claims 11-17, further comprising:

the data receiving module is configured to respond to the vehicle end to execute the corresponding data acquisition task according to the task list after the task issuing module sends the task list to the vehicle end, and receive the automatic driving data acquired by the vehicle end.

19. A data acquisition device, comprising:

20. The apparatus of claim 19, further comprising:

21. An electronic device, comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.

22. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-10.

23. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-10.

24. An autonomous vehicle comprising the electronic device of claim 21.