An automatic driving application data processing method, device, equipment and medium

Abstract

The application provides a kind of automatic driving application data processing method, it is characterized in that, method includes: collecting original data in vehicle driving distance;According to the data cleaning rule of preestablished, obtain normal original data, record as normal data;Store original data and normal data to original database;Extract normal data in original database, preestablished vehicle module function information, different vehicle module function information associated normal data is stored in different sub-database, and record as general sample data;In a sub-database, according to the vehicle module kind information of preestablished, different kinds of general sample data are recorded as different special sample data;Special sample data training, generate automatic driving application data processing model data.In the application, the processing efficiency of automatic driving application data can be improved, and accuracy is considered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, in particular to a processing method and device of automatic driving application data, equipment and medium. BACKGROUND

[0002] Data closed-loop control has been applied in the field of automatic driving. Traditional software manufacturers collect and analyze abnormal data in the use process of users by engineers to solve the defects and vulnerabilities in traditional software design, thereby optimizing products and improving user experience. However, when the data closed-loop system involves many types and large quantities of data, and different functional modules use different data management and application software, although the compatibility problem of data application link can be solved, data and sample management of data-driven algorithms are not performed. When facing an automatic driving system with more complex function architecture, the demand for rapid data call cannot be met, and there is room for improvement. SUMMARY

[0003] In view of the above-mentioned shortcomings of the prior art, the present application provides a method for solving the above technical problems.

[0004] The present application provides a processing method of automatic driving application data, which comprises:

[0005] Collecting original data in the driving distance of a vehicle;

[0006] According to the preset data cleaning rule, the normal original data is obtained and recorded as normal data;

[0007] Storing the original data and normal data in an original database;

[0008] Extracting the normal data in the original database, presetting vehicle module function information, storing the normal data associated with different vehicle module function information in different sub-databases, and recording as general sample data;

[0009] In one of the sub-databases, according to the preset vehicle module type information, the general sample data of different types is recorded as different specific sample data;

[0010] Training the specific sample data to generate processing model data of automatic driving application data.

[0011] In an embodiment of the present application, after the step of collecting original data in the driving distance of a vehicle, the method comprises:

[0012] Presetting a data amount threshold value, comparing the data amount of the original data with the size of the data amount threshold value;

[0013] When the data amount of the original data is greater than the data amount threshold, the original data is uploaded to the cloud server after being stored locally;

[0014] When the data amount of the original data is less than or equal to the data amount threshold, the original data is uploaded to the cloud server in real time.

[0015] In an embodiment of the present application, when the original data and the normal data are stored in the original database, the data content and the data format of the original data and the normal data are maintained.

[0016] In an embodiment of the present application, the normal data in the original database is extracted, vehicle module function information is preset, the normal data associated with different vehicle module function information is stored in different sub-databases, the data format of the normal data is changed, and the normal data is recorded as general sample data.

[0017] In an embodiment of the present application, each of the special sample data corresponds to an algorithm, the special sample data is trained, and a processing model of automatic driving application data is generated.

[0018] In an embodiment of the present application, after the step of training the special sample data to generate the processing model of automatic driving application data, the method further comprises:

[0019] The processing model of automatic driving application data is applied to an automatic driving vehicle to generate automatic driving data.

[0020] In an embodiment of the present application, the original data includes structured data and unstructured data.

[0021] The present application also provides a road condition refreshing device, which comprises:

[0022] A collection unit is configured to collect original data in a driving route of a vehicle.

[0023] A recording unit is configured to obtain normal original data according to a preset data cleaning rule and record the normal original data as normal data.

[0024] A storage unit is configured to store the original data and the normal data in an original database.

[0025] An extraction unit is configured to extract the normal data in the original database, preset vehicle module function information, store the normal data associated with different vehicle module function information in different sub-databases, and record the normal data as general sample data.

[0026] A saving unit is configured to record different general sample data as different special sample data according to preset vehicle module type information in a sub-database.

[0027] The generating unit is configured to train the unique sample data to generate processing model data of the autonomous driving application data.

[0028] The application can also provide an electronic device, comprising:

[0029] one or more processors;

[0030] a storage device configured to store one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the processing method of the autonomous driving application data according to any one of the above.

[0031] The application can also provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor of a computer, the computer executes the processing method of the autonomous driving application data according to any one of the above.

[0032] The application has the beneficial effect of improving the processing efficiency of the autonomous driving application data and ensuring the accuracy of the processing of the autonomous driving application data.

[0033] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the application. BRIEF DESCRIPTION OF DRAWINGS

[0034] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the application and, together with the specification, serve to explain the principles of the application. Obviously, the drawings in the following description are only some embodiments of the application, and those skilled in the art can obtain other drawings from these drawings without creative labor. In the drawings:

[0035] Figure 1 is a schematic diagram of an application environment of the processing method of the autonomous driving application data according to an exemplary embodiment of the application;

[0036] Figure 2 is a flowchart of the processing method of the autonomous driving application data according to an exemplary embodiment of the application;

[0037] Figure 3 is Figure 2 the flowchart of step S220 in the embodiment shown;

[0038] Figure 4 is a schematic diagram of the architecture of the processing method of the autonomous driving application data according to an exemplary embodiment of the application;

[0039] Figure 5 is a block diagram of a processing device for automatic driving application data according to an example embodiment of the present application;

[0040] Figure 6 A structural schematic diagram of a computer system of an electronic device suitable for implementing embodiments of the present application is shown. DETAILED DESCRIPTION

[0041] The present application will be described with reference to the attached drawings and preferred embodiments, and other advantages and effects of the present application will be easily understood by those skilled in the art from the disclosure of this specification. The present application can also be implemented or applied in other different specific embodiments, and various modifications or changes can be made to the details of the specification based on different perspectives and applications, without departing from the spirit of the present application. It should be understood that the preferred embodiments are only for the purpose of illustrating the present application, and are not intended to limit the scope of protection of the present application.

[0042] It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present application, and only the components related to the present application are shown in the diagrams, rather than being drawn according to the number, shape and size of the components when actually implemented. The actual implementation of each component may be a random change in shape, number and proportion, and the layout pattern of the components may also be more complex.

[0043] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present application, however, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details, and in other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail, to avoid making the embodiments of the present application difficult to understand.

[0044] First of all, it should be noted that the automatic driving system uses advanced communication, computer, network and control technology to realize real-time and continuous control of the train. Using modern communication means, directly facing the train, bidirectional data communication between the train and the ground can be realized, the transmission rate is fast, the information amount is large, the subsequent tracking train and the control center can timely know the exact position of the preceding train, so that the operation management is more flexible, the control is more effective, and it is more suitable for the needs of automatic driving of the train. The automatic driving system refers to a train operation system in which the work performed by the train driver is fully automated and highly centralized controlled. The automatic driving system has functions such as automatic wake-up and sleep of the train, automatic entry and exit of the parking lot, automatic cleaning, automatic driving, automatic parking, automatic opening and closing of the train door, automatic fault recovery, etc., and has multiple operation modes such as normal operation, degraded operation, and operation interruption. The realization of full automatic operation can save energy and optimize the reasonable matching of system energy consumption and speed.

[0045] Figure 1is a schematic diagram of an implementation environment of a method for processing automatic driving application data according to an example embodiment of the present application. As shown in Figure 1 In some embodiments, the current user of the client can send an input instruction to the server through the communication network. After receiving the input instruction of the client, the server can generate a code file based on the car platform. Wherein, Figure 1 The server shown can be any terminal device supporting the installation of navigation map software, such as a smartphone, an on-board computer, a tablet computer, a notebook computer, or a wearable device, but is not limited thereto. Figure 1 The server shown is a server, which can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms, and is not limited herein. The client can communicate with the server through a wireless network such as 3G (third generation mobile information technology), 4G (fourth generation mobile information technology), 5G (fifth generation mobile information technology), and the like, and the present application is not limited thereto.

[0046] In some embodiments, the data loop system involves a large number of data types and a large amount of data, and the data management and application software used by each functional module is different. Although it can solve the compatibility problem of data application link, it does not manage data and samples for data-driven algorithms. When facing a more complex automatic driving system, it cannot meet the demand for rapid data call, and there is room for improvement. To solve these problems, the embodiments of the present application respectively propose a method for processing automatic driving application data, an automatic driving application data processing device, an electronic device, a computer readable storage medium, and a computer program product, which will be described in detail below.

[0047] Figure 2 is a schematic diagram of an implementation environment of a method for processing automatic driving application data according to an example embodiment of the present application. In some embodiments, the method can be applied to Figure 1 The implementation environment shown, and specifically executed by the client in the implementation environment. It should be understood that the method can also be applied to other example implementation environments and specifically executed by devices in other implementation environments, and the present embodiment does not limit the implementation environment to which the method is applied.

[0048] In some embodiments, the client to which the method for generating a code file based on a vehicle platform disclosed in an exemplary embodiment can be installed with an SDK (Software Development Kit, a collection of development tools for building applications for a specific software package, software framework, operating system, etc.), and the method disclosed in the embodiment is implemented as one or more functions provided by the SDK.

[0049] Referring to Figure 2 , Figure 2 is a flowchart of a processing method for target tracking according to an exemplary embodiment of the present application. The method can be applied to the implementation environment shown in Figure 1 , and is specifically executed by an intelligent terminal in the implementation environment. It should be understood that the method can also be applied to other exemplary implementation environments and specifically executed by devices in other implementation environments, and the present embodiment does not limit the implementation environment to which the method is applied.

[0050] In an exemplary embodiment, the intelligent terminal to which the method for processing autonomous driving application data disclosed in the embodiment can be installed with a navigation SDK (Software Development Kit, a collection of development tools for building applications for a specific software package, software framework, operating system, etc.), and the method disclosed in the embodiment is implemented as one or more functions provided by the navigation SDK.

[0051] As shown in Figure 2 , in an exemplary embodiment, the road condition refreshing method includes at least steps S210 to S260, which are described in detail as follows:

[0052] Step S210: Collecting original data in the driving route of the vehicle.

[0053] In some embodiments, during the driving of the vehicle, after starting the automatic driving system, the data of each functional module can be recorded by using the automatic driving function framework. Each functional module includes but is not limited to fusion target, fusion positioning module, bus module, positioning module, high-precision map and other data information. The automatic driving function framework can use different frameworks according to different integration methods. A typical framework example is ROS (Robot Operating System). The content of data collection can be the original data recorded by the framework. The original data is generally serialized binary data. A typical example of serialization can be performed by using protobuf (protocol buffers). ProtoBuf is a language-independent, platform-independent and extensible method for serializing structured data, which can be used for (data) communication protocols, data storage, etc. ProtoBuf predefines the name and order of the transmission fields, so that only the type tag and binary value of each field need to be recorded in the transmission structure. The original data includes structured data and unstructured data. Structured data is simply a database. It is easier to understand in a typical scenario, such as enterprise ERP (Enterprise Resource Planning), financial system, medical HIS (Hospital Information System) database, education card, government administrative examination and approval, and other core databases. Unstructured data is data with irregular or incomplete structure, without a predefined data model, and is not convenient to represent by a two-dimensional logical table of a database. Unstructured data includes all formats of office documents, texts, pictures, HTML (HyperText Markup Language), various reports, images and audio / video information, etc.

[0054] In step S220, normal original data is obtained according to a preset data cleaning rule, and is recorded as normal data.

[0055] In some embodiments, data cleaning refers to the last procedure for finding and correcting identifiable errors in data files, including checking data consistency, processing invalid values and missing values, etc. Unlike questionnaire review, data cleaning after input is generally completed by computer rather than manually. Data cleaning is a process of re-examining and checking data, aiming to delete duplicate information, correct existing errors, and provide data consistency. The data cleaning rule can be preset. According to the preset data cleaning rule, duplicate information and error information are processed, and then normal original data is obtained and recorded as normal data.

[0056] In step S230, the original data and the normal data are stored in an original database.

[0057] In some embodiments, the database system (Database System) is a system composed of a database and its management software. The database system is an ideal data processing system developed to meet the needs of data processing, and is also a software system that provides data for an actual executable storage, maintenance and application system, and is a collection of storage media, processing objects and management systems. Raw data and normal data can be stored in the raw database. For example, when storing the raw data and the normal data in the raw database, the data content and the data format of the raw data and the normal data are maintained.

[0058] Step S240, extracting the normal data in the raw database, presetting vehicle module function information, storing the normal data associated with different vehicle module function information into different sub-databases, and recording as general sample data.

[0059] In some embodiments, the vehicle module function can be divided according to perception, prediction, decision, planning, etc., and the fields, contents, time ranges, etc. of the data can be modified according to the function module requirements and version information, etc. When the function changes greatly, the sub-database can be redesigned and the data can be extracted again. And the normal data associated with different vehicle module function information can be stored in different sub-database. For example, the normal data associated with the decision function can be stored in a sub-database, and the normal data associated with the planning function can be stored in another sub-database. And the normal data associated with the vehicle module function information can be recorded as general sample data. For example, the normal data in the raw database is extracted, the vehicle module function information is preset, the normal data associated with different vehicle module function information is stored in different sub-databases, the data format of the normal data is changed, and the general sample data is recorded.

[0060] Step S250, in a sub-database, according to the preset vehicle module type information, the general sample data of different types is recorded as different specific sample data.

[0061] In some embodiments, in the same sub-database, the general sample data of different types can be recorded as different specific sample data according to the preset vehicle module type information. For example, the vehicle module type information can include speed, heading angle, etc. The general sample data corresponding to the speed is recorded as the specific sample data of the speed, and the general sample data corresponding to the heading angle is recorded as the specific sample data of the heading angle.

[0062] Step S260, training the specific sample data to generate processing model data of automatic driving application data.

[0063] In some embodiments, the unique sample data is trained to generate processing model data of autonomous driving application data. The common sample data generates and stores common features on the one hand, and takes the union of data requirements of different algorithms to store, and since the downstream algorithm may be frequently iterated in feature construction, the common sample data only provides the original data required by the algorithm-specific features. The unique sample data is only applicable to the training and evaluation of a specific algorithm, and due to the iterative nature of algorithm development, the sample generation method may be frequently updated. Since data can be continuously obtained after the autonomous driving system is completed, the algorithm can be iterated in a periodic update manner to improve the algorithm performance. In actual application, due to the limitation of the vehicle terminal platform, the model needs to be quantized and accelerated, and the software and hardware adaptation method is more suitable for real-time application of the vehicle terminal. According to each algorithm corresponding to the unique sample data, the unique sample data is trained to generate a processing model of autonomous driving application data. The processing model of autonomous driving application data is applied to an autonomous driving vehicle to generate autonomous driving data.

[0064] Referring to Figure 3 In the processing method of autonomous driving application data, the processing step of storing the original data. In some embodiments, step S310 can be performed first, and a data amount threshold is preset. Secondly, step S320 can be performed, and the size of the data amount of the original data and the data amount threshold can be compared. When the data amount of the original data is less than or equal to the data amount threshold, it can be uploaded to the cloud server through real-time transmission. In step S330, when the data amount of the original data is greater than the data amount threshold, it can be uploaded to the cloud server through real-time transmission.

[0065] Referring to Figure 4 As shown in the figure, it is a framework schematic diagram of an autonomous driving application data processing device. In some embodiments, first, the data of the autonomous driving vehicle 401 can be collected 402 and recorded as original data. Secondly, the original data can be processed by uploading to the cloud 403, that is, the original data is uploaded to the cloud for processing. Secondly, the stored original data can be parsed 404 through cloud storage, and the original data can be restored to business data. Secondly, the original data can be cleaned 405, and the normal data retained after the data cleaning 405 processing can be stored in the original database 406. Secondly, the normal data in the original database 406 can be extracted, the vehicle module function information can be preset, the normal data associated with different vehicle module function information can be stored in different sub-databases 407, and the normal data can be recorded as common sample data 408. In a sub-database 407, according to the preset vehicle module type information, different types of common sample data 408 are recorded as different unique sample data 409. The unique sample data 409 is trained to generate processing model data 412 of autonomous driving application data.

[0066] Referring to Figure 5 As shown in the drawings, the application provides an automatic driving application data processing device, in some embodiments, the automatic driving application data processing device comprises a collection unit 801, a recording unit 802, a storage unit 803, an extraction unit 804, a saving unit 805 and a generation unit 806. The functions of each module are described in detail as follows.

[0067] The collection unit 801 is configured to collect original data in a vehicle driving route. The recording unit 802 is configured to obtain normal original data according to a preset data cleaning rule and record the normal original data as normal data. The storage unit 803 is configured to store the original data and the normal data to an original database. The extraction unit 804 is configured to extract the normal data in the original database, preset vehicle module function information, store the normal data associated with different vehicle module function information to different sub-databases, and record the normal data as general sample data. The saving unit 805 is configured to record different types of general sample data as different specific sample data in a sub-database according to preset vehicle module type information. The generation unit 806 is configured to train the specific sample data and generate automatic driving application data processing model data.

[0068] The specific limitations of the processing device for target tracking can refer to the limitations of the automatic driving application data processing method described above, which will not be repeated here. Each module in the automatic driving application data processing device described above can be realized by software, hardware and their combinations in whole or in part. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0069] Figure 6 The structure of the computer system of the electronic device suitable for implementing the embodiments of the application is shown. It should be noted that, Figure 6 The computer system 600 of the electronic device shown is only an example and should not limit the functions and use range of the embodiments of the application.

[0070] As Figure 6As shown, the computer system 600 includes a central processing unit (CPU) 601 which can perform various suitable actions and processes in accordance with programs stored in a read-only memory (ROM) 602 or loaded from the storage section 608 into a random access memory (RAM) 603, such as performing the methods described in the above embodiments. Various programs and data required for the operation of the system are also stored in the RAM 603. The CPU 1201, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0071] Connected to the I / O interface 605 are an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as necessary. A removable recording medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 610 as necessary, so that a computer program read therefrom is installed into the storage section 608 as necessary.

[0072] In particular, in accordance with the embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing a computer program for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from the removable recording medium 611. When the computer program is executed by the central processing unit (CPU) 601, various functions defined in the system of the present application are performed.

[0073] It should be noted that the computer-readable medium in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example, be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples of the computer-readable storage medium can include, but are not limited to, 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), a 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 thereof. In this application, the computer-readable signal medium can include a data signal propagated in a baseband or as a carrier wave in a propagated data signal, in which the computer-readable computer program is carried. Such a propagated data signal can take on many forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit the program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted in any suitable medium, including but not limited to wireless, wired, or the like, or any suitable combination thereof.

[0074] The flowcharts and block diagrams in the drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Each block in the flowcharts or block diagrams can represent a module, a program segment, or a portion of code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in a different order than that shown in the figures. For example, two blocks noted in succession can actually be executed substantially concurrently, or they can sometimes be executed in reverse order, depending on the functionality involved. It should also be noted that each block in the flowcharts or block diagrams, and combinations of blocks in the flowcharts or block diagrams, can be implemented by special-purpose hardware-based systems, which perform the specified functions or operations, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0075] The units described in the embodiments of the present application can be implemented in the form of software, or can be implemented in the form of hardware, and the described units can also be arranged in a processor. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0076] Another aspect of the present application also provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor of a computer, so that the computer executes the processing method of the autonomous driving application data as described above. The computer readable storage medium can be included in the electronic device described in the above embodiments, or can exist separately without being assembled into the electronic device.

[0077] Another aspect of the present application also provides a computer program product or a computer program, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the processing method of the autonomous driving application data provided in each of the above embodiments.

[0078] The above embodiments only exemplarily illustrate the principles and effects of the present application, and are not used to limit the present application. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical thought of the present application should be covered by the claims of the present application.

Claims

1. A processing method of autonomous driving application data, characterized by, The method comprises: Collecting original data in a vehicle driving route; Obtaining normal original data according to a preset data cleaning rule and recording as normal data; Storing the original data and the normal data to an original database; Extracting the normal data in the original database, presetting vehicle module function information, storing the normal data associated with different vehicle module function information to different sub-databases, changing the data grid format of the normal data, and recording as general sample data; wherein the vehicle module function at least includes decision function and planning function, the normal data associated with the decision function is stored in a sub-database, and the normal data associated with the planning function is stored in another sub-database; the vehicle module function is divided according to perception, prediction, decision and planning, and the fields, contents and time range of data can be modified according to the function module demand and version information, when the function changes, the sub-database is redesigned and the data is extracted again; In a sub-database, different types of general sample data are recorded as different specific sample data according to preset vehicle module type information; wherein in the same sub-database, different types of general sample data are recorded as different specific sample data according to preset vehicle module type information; the vehicle module type information at least includes speed and heading angle, the general sample data corresponding to the speed is recorded as the specific sample data of the speed, and the general sample data corresponding to the heading angle is recorded as the specific sample data of the heading angle; Training the specific sample data to generate processing model data of automatic driving application data. 2.The method of Claim 1, wherein, After the step of collecting original data in a vehicle driving route, comprising: Presetting a data amount threshold, comparing the data amount of the original data with the size of the data amount threshold; When the data amount of the original data is greater than the data amount threshold, uploading the original data to the cloud server after storing locally; When the data amount of the original data is less than or equal to the data amount threshold, uploading the original data to the cloud server in real time. 3.The method of Claim 1, wherein, When storing the original data and the normal data to the original database, maintaining the data content and data format of the original data and the normal data. 4.The method of Claim 1, wherein, According to each specific sample data corresponding to an algorithm, the specific sample data is trained to generate processing model of automatic driving application data. 5.The method of Claim 1, wherein, After the step of training the specific sample data to generate processing model of automatic driving application data, comprising: Applying the processing model of automatic driving application data to an automatic driving vehicle to generate automatic driving data. 6.The method of Claim 1, wherein, The original data includes structured data and unstructured data.

7. A road condition refreshing device characterized by comprising: The device comprises: A collection unit for collecting original data in a vehicle driving route; A recording unit for obtaining normal original data according to a preset data cleaning rule and recording as normal data; A storage unit for storing the original data and the normal data to an original database; The extraction unit is configured to extract the normal data in the original database, preset vehicle module function information, store the normal data associated with different vehicle module function information into different sub-databases, change the data grid format of the normal data, and record as general sample data; wherein the vehicle module function at least includes decision function and planning function, the normal data associated with the decision function is stored into a sub-database, and the normal data associated with the planning function is stored into another sub-database; the vehicle module function is divided according to perception, prediction, decision, and planning, and the fields, contents, and time ranges of the data can be modified according to the function module demand and version information, when the function is changed, the sub-database is redesigned and the data is extracted again; The saving unit is configured to record different specific sample data from different general sample data according to preset vehicle module type information in a sub-database; wherein in the same sub-database, different general sample data is recorded as different specific sample data according to preset vehicle module type information; the vehicle module type information at least includes speed and heading angle, the general sample data corresponding to the speed is recorded as the specific sample data of the speed, and the general sample data corresponding to the heading angle is recorded as the specific sample data of the heading angle; The generating unit is configured to train the specific sample data to generate processing model data of the automatic driving application data.

8. An electronic device, comprising: The electronic device comprises: one or more processors; a storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, the electronic device implements the processing method of the automatic driving application data as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, A computer program is stored thereon, when the computer program is executed by the processor of the computer, the computer executes the processing method of the automatic driving application data as claimed in any one of claims 1 to 6.

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