High-precision map data processing method, device, electronic device and storage medium

Abstract

The present disclosure provides a high-precision map data processing method, device, electronic device and storage medium, which relate to the field of computer technology, and in particular to the field of autonomous driving and high-precision maps. The specific implementation scheme is: in response to an input track name, obtain track information corresponding to the track name; determine a first address, a second address and an index format corresponding to the track information according to the track information; the first address includes the address of the source database corresponding to the track information, and the second address includes the address of the target database corresponding to the track information; submit a Spark job according to the storage path, the first address, the second address and the index format of the diversion program; call the Spark engine to run the Spark job to execute the diversion program, so as to read the high-precision map data that conforms to the index format from the source database at the first address as it is, and output it as it is to the target database at the second address.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to the field of autonomous driving and high-precision maps. Background Art

[0002] High-precision maps, also known as high-definition maps, are generally electronic maps with higher accuracy and more data dimensions. They include not only road information, but also surrounding static information related to traffic, such as lane information, traffic signs, road location information, etc., which can provide all-round assistance for autonomous driving applications.

[0003] In the production process of high-precision maps, the collected point clouds and images are needed as input data. The required high-precision maps can be obtained by performing algorithmic processing on the input data. Specifically, the algorithm needs to be continuously tested based on the input data collected multiple times so that the algorithm can cover as many scenarios as possible. Specifically, the algorithm can be optimized based on the results of algorithm processing and manual processing, and finally the optimized algorithm is used to produce high-precision maps. In order to ensure that the algorithm test does not affect the production task of high-precision maps, the test environment and the production environment need to be isolated. The data in the production environment is permanently saved without manual deletion. The data in the test environment increases rapidly due to continuous testing. In order to avoid insufficient storage space, data needs to be deleted regularly.

[0004] The raw data of point clouds and images collected by dedicated acquisition equipment cannot be directly used by the algorithm. The raw data needs to be converted into a data format that the algorithm can recognize. Directing the converted data in the production environment to the test environment can save the time of format conversion in the test environment. Summary of the invention

[0005] The present invention provides a method, device, electronic device and storage medium for processing high-precision map data.

[0006] According to one aspect of the present disclosure, a method for processing high-precision map data is provided, comprising: in response to an input track name, obtaining track information corresponding to the track name; determining a first address, a second address, and an index format corresponding to the track information according to the track information; wherein the first address includes an address of a source database corresponding to the track information, and the second address includes an address of a target database corresponding to the track information; submitting a Spark job according to a storage path of a diversion program, the first address, the second address, and the index format; wherein the diversion program is used to complete the operations of reading and outputting data as is; calling a Spark engine to run the Spark job to execute the diversion program under the storage path, so as to read the high-precision map data that conforms to the index format as is from the source database at the first address, and output the read high-precision map data as is to the target database at the second address.

[0007] According to another aspect of the present disclosure, a high-precision map data processing device is provided, including: an acquisition module, for acquiring trajectory information corresponding to an input trajectory name in response to the trajectory name; a determination module, for determining a first address, a second address and an index format corresponding to the trajectory information according to the trajectory information; wherein the first address includes an address of a source database corresponding to the trajectory information, and the second address includes an address of a target database corresponding to the trajectory information; a submission module, for submitting a Spark job according to a storage path of a diversion program, the first address, the second address and the index format; wherein the diversion program is used to complete the operations of reading and outputting data as is; a calling module, for calling a Spark engine to run the Spark job, so as to execute the diversion program under the storage path, so as to realize reading the high-precision map data that conforms to the index format as is from the source database at the first address, and outputting the read high-precision map data as is to the target database at the second address.

[0008] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned high-precision map data processing method.

[0009] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the above-mentioned high-precision map data processing method.

[0010] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the above-mentioned high-precision map data processing method when executed by a processor.

[0011] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0013] Figure 1 is a flowchart of a method for processing high-precision map data according to an embodiment of the present disclosure;

[0014] Figure 2 is a schematic diagram of a method for processing high-precision map data according to an embodiment of the present disclosure;

[0015] Figure 3 is a schematic diagram of a high-precision map data processing method according to another embodiment of the present disclosure;

[0016] Figure 4 is a block diagram of a high-precision map data processing device according to an embodiment of the present disclosure;

[0017] Figure 5 It is a block diagram of an electronic device used to implement the high-precision map data processing method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0018] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0020] According to an embodiment of the present disclosure, an embodiment of a high-precision map data processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0021] Figure 1 is a flowchart of a method for processing high-precision map data according to an embodiment of the present disclosure. Figure 1 As shown, the method includes the following steps S101 to S104:

[0022] Step S101: In response to an input track name, obtain track information corresponding to the track name.

[0023] The track involved in the embodiment of the present disclosure is used to characterize the change of the driving path of the vehicle. In a specific implementation, different tracks can be obtained by collecting the driving paths of different vehicles, and the track name is used to uniquely characterize the identity information of the track.

[0024] In practical applications, the track information may include track name, product line, reference pose, vehicle arm value and other information. By inputting the track name, the complete track information of the corresponding track can be obtained.

[0025] Step S102: Determine a first address, a second address, and an index format corresponding to the track information according to the track information, wherein the first address includes an address of a source database corresponding to the track information, and the second address includes an address of a target database corresponding to the track information.

[0026] Among them, the source database corresponding to the trajectory information refers to the source database where the high-precision map data corresponding to the trajectory information is located, and the index format corresponding to the trajectory information refers to the index format of the high-precision map data corresponding to the trajectory information in the source database.

[0027] Step S103, submitting a Spark job according to the storage path of the diversion program, the first address, the second address and the index format. The diversion program is used to complete the operation of reading and outputting data as it is, and the storage path of the diversion program is specifically the storage path of the diversion program in the distributed network file system AFS.

[0028] It should be noted that different track names correspond to different Spark jobs.

[0029] Step S104, calling the Spark engine to run the Spark job to execute the diversion program under the storage path, so as to read the high-precision map data that conforms to the index format from the source database at the first address as is, and output the read high-precision map data as is to the target database at the second address.

[0030] Among them, the Spark engine is a fast and general computing engine designed specifically for large-scale data processing. Figure 2 As shown, the Spark engine is called to run the Spark job, the diversion program is found according to the storage path of the diversion program, and then the diversion program is executed to complete the operation of reading the high-precision map data that conforms to the index format from the source database as is, and outputting the read high-precision map data as is to the target database.

[0031] In the disclosed embodiment, by calling the Spark engine to run a Spark job to execute a diversion program for reading and outputting the high-precision map data as is, it is possible to divert the high-precision map data from the source database to the target database.

[0032] In an optional embodiment, the Airflow platform and the Spark engine are combined to realize the diversion of high-precision map data. Among them, the Airflow platform is a visual distributed task scheduling platform that provides workflow orchestration based on the DAG directed acyclic graph. Step S102 specifically includes the following steps S1021 to S1022:

[0033] Step S1021: Generate a traffic diversion task corresponding to the trajectory information, and submit the traffic diversion task to the Airflow platform.

[0034] In a specific implementation, when multiple trajectory names are input, after obtaining the trajectory information corresponding to each trajectory name, a diversion task can be uniformly generated and submitted to the Airflow platform once.

[0035] Step S1022: Call the Airflow platform to determine the first address, the second address, and the index format corresponding to the trajectory information according to the diversion task.

[0036] In the disclosed embodiment, the Airflow platform is utilized to determine the address of the source database corresponding to the trajectory information, the address of the target database corresponding to the trajectory information, and the index format corresponding to the trajectory information according to the diversion task. When the diversion task corresponds to multiple trajectory information, that is, corresponds to multiple trajectories, the address of the source database, the address of the target database, and the index format corresponding to each trajectory information can be determined in parallel, thereby improving the efficiency of high-precision map data diversion.

[0037] In an optional embodiment, the high-precision map data includes point cloud data and image data. The source database corresponding to the trajectory information includes a source database where the point cloud data corresponding to the trajectory information is located and a source database where the image data corresponding to the trajectory information is located, and the source database corresponds to the target database one-to-one. The index format corresponding to the trajectory information includes an index format of the point cloud data corresponding to the trajectory information and an index format of the image data corresponding to the trajectory information.

[0038] Among them, for the same trajectory information, that is, the same trajectory, the source database where the point cloud data is located is different from the source database where the image data is located, and the point cloud data and image data need to be diverted separately to achieve the diversion of high-precision map data. Specifically, the format of the point cloud data can be pcd format or las format, etc., and the format of the image data can be img format or png format, etc.

[0039] In a specific example, the index format of point cloud data consists of the base pose, trajectory name, point cloud data category and timestamp, and the index format of image data consists of the base pose, trajectory name, image data category and timestamp. The timestamp refers to the acquisition time of a frame of data in the trajectory. During the operation of the Spark job, the high-precision map data can be read and output as is from the source database in the following way: fix the base pose, trajectory name and data category, enumerate the timestamp from 0 to the maximum value in sequence, and if an index that matches the index format is matched, a frame of point cloud data or image data is found, and it is read and output as is.

[0040] In an optional embodiment, step S1022 specifically includes: calling the Airflow platform to generate a first flow diversion processing module and a second flow diversion processing module corresponding to the trajectory information according to the flow diversion task.

[0041] Among them, the first traffic diversion processing module is used to determine the address of the source database where the point cloud data corresponding to the trajectory information is located, the address of the target database corresponding to the source database where the point cloud data is located, and the index format of the point cloud data corresponding to the trajectory information; the second traffic diversion processing module is used to determine the address of the source database where the image data corresponding to the trajectory information is located, the address of the target database corresponding to the source database where the image data is located, and the index format of the image data corresponding to the trajectory information.

[0042] In a specific implementation, a Spark job is submitted according to the storage path of the diversion program, the address of the source database where the point cloud data corresponding to the trajectory information is located, the address of the target database corresponding to the source database where the point cloud data is located, and the index format of the point cloud data corresponding to the trajectory information, and another Spark job is submitted according to the storage path of the diversion program, the address of the source database where the image data corresponding to the trajectory information is located, the address of the target database corresponding to the source database where the image data is located, and the index format of the image data corresponding to the trajectory information. Using different Spark jobs to divert point cloud data and image data in parallel can improve the diversion efficiency of high-precision map data.

[0043] like Figure 3 As shown, different trajectory information only needs to submit a diversion task to the Airflow platform once. The Airflow platform generates two diversion processing modules corresponding to each trajectory information according to the diversion task, and respectively determines the address and index format of the source database where the point cloud data corresponding to the trajectory information is located, as well as the address and index format of the source database where the image data corresponding to the trajectory information is located. Specifically, the Airflow platform can be used to configure the resources for the running of Spark jobs. The resources for the running of Spark jobs include the maximum concurrency of all subtasks, the memory size and number of CPUs allocated when each subtask runs the diversion program, etc. Figure 3 In the example shown, a Spark job is divided into 3000 subtasks, all of which execute the diversion program concurrently, each subtask diverts a part of the point cloud data or image data, and all of the subtasks divert all of the point cloud data or all of the image data.

[0044] According to an embodiment of the present disclosure, a high-precision map data processing device embodiment is also provided, wherein: Figure 43 is a schematic diagram of a high-precision map data processing device according to an embodiment of the present disclosure, the device comprising: an acquisition module 301, a determination module 302, a submission module 303 and a call module 304. The acquisition module 301 is used to respond to an input track name and obtain track information corresponding to the track name. The determination module 302 is used to determine a first address, a second address and an index format corresponding to the track information according to the track information. The first address includes the address of the source database corresponding to the track information, and the second address includes the address of the target database corresponding to the track information. The submission module 303 is used to submit a Spark job according to the storage path of the diversion program, the first address, the second address and the index format. The diversion program is used to complete the operation of reading and outputting data as is. The call module 304 is used to call the Spark engine to run the Spark job to execute the diversion program under the storage path, so as to read the high-precision map data that conforms to the index format as is from the source database at the first address, and output the read high-precision map data as is to the target database at the second address.

[0045] It should be noted that the above-mentioned acquisition module 301, determination module 302, submission module 303 and calling module 304 correspond to steps S101 to S104 in the above-mentioned embodiments. These four modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0046] In an optional embodiment, the determination module includes: a generation unit, used to generate a diversion task corresponding to the trajectory information and submit the diversion task to the Airflow platform; and a calling unit, used to call the Airflow platform to determine the first address, the second address and the index format corresponding to the trajectory information according to the diversion task.

[0047] In an optional embodiment, the high-precision map data includes point cloud data and image data; the source database corresponding to the trajectory information includes a source database where the point cloud data corresponding to the trajectory information is located and a source database where the image data corresponding to the trajectory information is located, and the source database corresponds to the target database one-to-one; the index format corresponding to the trajectory information includes an index format of the point cloud data corresponding to the trajectory information and an index format of the image data corresponding to the trajectory information.

[0048] In an optional embodiment, the calling unit is specifically used to call the Airflow platform to generate a first flow diversion processing module and a second flow diversion processing module corresponding to the trajectory information according to the flow diversion task;

[0049] Among them, the first traffic diversion processing module is used to determine the address of the source database where the point cloud data corresponding to the trajectory information is located, the address of the target database corresponding to the source database where the point cloud data is located, and the index format of the point cloud data corresponding to the trajectory information; the second traffic diversion processing module is used to determine the address of the source database where the image data corresponding to the trajectory information is located, the address of the target database corresponding to the source database where the image data is located, and the index format of the image data corresponding to the trajectory information.

[0050] The device embodiments described above are merely illustrative, and the modules or units described as separate components may be physically separated or not, and may be located in one place or distributed to multiple network units. Some or all of the modules or units may be selected according to actual needs to achieve the purpose of the disclosed solution.

[0051] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0052] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0053] Figure 5 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0054] like Figure 5 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0055] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0056] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as a high-precision map data processing method. For example, in some embodiments, the high-precision map data processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the high-precision map data processing method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the high-precision map data processing method in any other appropriate manner (e.g., by means of firmware).

[0057] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0058] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0059] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0060] To provide interaction with a user, the systems and techniques described herein 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 the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0061] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0062] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0063] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0064] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A high-precision map data processing method, comprising: In response to the input track name, obtaining track information corresponding to the track name; Determine a first address, a second address, and an index format corresponding to the trajectory information according to the trajectory information; wherein the first address includes an address of a source database corresponding to the trajectory information, and the second address includes an address of a target database corresponding to the trajectory information; Submitting a Spark job according to the storage path of the diversion program, the first address, the second address, and the index format; wherein the diversion program is used to complete the operation of reading and outputting the data as it is; Call the Spark engine to run the Spark job to execute the diversion program under the storage path, so as to read the high-precision map data that conforms to the index format from the source database at the first address as is, and output the read high-precision map data as is to the target database at the second address.

2. The method according to claim 1, wherein: The determining, according to the track information, the first address, the second address, and an index format corresponding to the track information includes: Generate a traffic diversion task corresponding to the trajectory information, and submit the traffic diversion task to the Airflow platform; The Airflow platform is called to determine a first address, a second address, and an index format corresponding to the trajectory information according to the flow diversion task.

3. The method according to claim 2, wherein: The high-precision map data includes point cloud data and image data; The source database corresponding to the trajectory information includes a source database where the point cloud data corresponding to the trajectory information is located and a source database where the image data corresponding to the trajectory information is located, and the source database corresponds to the target database one by one; The index format corresponding to the trajectory information includes an index format of point cloud data corresponding to the trajectory information and an index format of image data corresponding to the trajectory information.

4. The method according to claim 3, wherein: The calling of the Airflow platform to determine the first address, the second address, and the index format corresponding to the track information according to the flow diversion task includes: Calling the Airflow platform to generate a first flow diversion processing module and a second flow diversion processing module corresponding to the trajectory information according to the flow diversion task; Among them, the first traffic diversion processing module is used to determine the address of the source database where the point cloud data corresponding to the trajectory information is located, the address of the target database corresponding to the source database where the point cloud data is located, and the index format of the point cloud data corresponding to the trajectory information; the second traffic diversion processing module is used to determine the address of the source database where the image data corresponding to the trajectory information is located, the address of the target database corresponding to the source database where the image data is located, and the index format of the image data corresponding to the trajectory information.

5. A high-precision map data processing device, comprising: An acquisition module, configured to acquire, in response to an input track name, track information corresponding to the track name; A determination module, configured to determine a first address, a second address, and an index format corresponding to the trajectory information according to the trajectory information; wherein the first address includes an address of a source database corresponding to the trajectory information, and the second address includes an address of a target database corresponding to the trajectory information; A submission module, used to submit a Spark job according to the storage path of the diversion program, the first address, the second address and the index format; wherein the diversion program is used to complete the operation of reading and outputting the data as it is; A calling module is used to call the Spark engine to run the Spark job to execute the diversion program under the storage path, so as to read the high-precision map data that conforms to the index format from the source database at the first address as is, and output the read high-precision map data as is to the target database at the second address.

6. The device according to claim 5, wherein: The determination module comprises: A generating unit, configured to generate a flow diversion task corresponding to the trajectory information, and submit the flow diversion task to the Airflow platform; A calling unit is used to call the Airflow platform to determine the first address, the second address and the index format corresponding to the trajectory information according to the diversion task.

7. The device according to claim 6, wherein: The high-precision map data includes point cloud data and image data; The source database corresponding to the trajectory information includes a source database where the point cloud data corresponding to the trajectory information is located and a source database where the image data corresponding to the trajectory information is located, and the source database corresponds to the target database one by one; The index format corresponding to the trajectory information includes an index format of point cloud data corresponding to the trajectory information and an index format of image data corresponding to the trajectory information.

8. The device according to claim 7, wherein: The calling unit is specifically used to call the Airflow platform to generate a first flow diversion processing module and a second flow diversion processing module corresponding to the trajectory information according to the flow diversion task; Among them, the first traffic diversion processing module is used to determine the address of the source database where the point cloud data corresponding to the trajectory information is located, the address of the target database corresponding to the source database where the point cloud data is located, and the index format of the point cloud data corresponding to the trajectory information; the second traffic diversion processing module is used to determine the address of the source database where the image data corresponding to the trajectory information is located, the address of the target database corresponding to the source database where the image data is located, and the index format of the image data corresponding to the trajectory information.

9. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

11. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 4.

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