Intelligent vehicle control method and system based on metadata

Abstract

The metadata-based intelligent automobile control method and system provided by the present application determines the automobile control data to be processed that includes the previously set constraint conditions for intelligent automobile assisted driving data; on the premise that the previously set constraint conditions of the automobile control data to be processed include the control conditions of the reference scene, based on the uninterrupted multiple automobile control events of the control conditions of the reference scene screened out from the previously set constraint conditions, the control index of the control condition of the reference scene in the previously set constraint conditions is determined, and the reference scene includes the previously set control condition scene that matches the automobile status items; on the premise that the control index does not meet the previously set driving instructions, the automobile status item parsing result of the control condition of the screened reference scene is generated. Through the above steps, the automobile can be accurately controlled, so that the precision and accuracy of the automobile intelligent control can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a metadata-based intelligent vehicle control method and system. Background Art

[0002] Metadata, also known as intermediary data or relay data, is data about data. It is mainly information describing data properties, used to support functions such as indicating storage location, historical data, resource search, and file records. Metadata is a kind of electronic catalog. In order to achieve the purpose of cataloging, it is necessary to describe and collect the content or characteristics of the data, thereby achieving the purpose of assisting data retrieval.

[0003] At present, metadata involves more and more technical fields. After metadata is specifically combined with smart car technology, there may be multiple different route situations in multiple scenarios, which may interfere with the control of the car. Therefore, it is difficult to ensure the reliability and accuracy of the car control. Summary of the invention

[0004] In order to improve the technical problems existing in the related technologies, the present application provides a metadata-based intelligent vehicle control method and system.

[0005] In a first aspect, a metadata-based intelligent vehicle control method is provided, the method at least comprising: determining vehicle control data to be processed that covers previously set constraints for intelligent vehicle assisted driving data; on the premise that the previously set constraints for the vehicle control data to be processed include control conditions of a reference scene, based on multiple uninterrupted vehicle control events that filter out the control conditions of the reference scene in the previously set constraints, determining control indicators of the control conditions of the reference scene in the previously set constraints, wherein the multiple uninterrupted vehicle control events cover the vehicle control data to be processed; the reference scene includes a previously set control condition scene that matches the vehicle status item; on the premise that the control indicator does not satisfy the previously set driving instruction, generating a vehicle status item parsing result of the filtered control conditions of the reference scene.

[0006] In an independently implemented embodiment, the determination of the vehicle control data to be processed that covers the previously set constraints for performing intelligent vehicle assisted driving data includes: based on the previously set assisted driving evaluation results, filtering out one vehicle control data to be processed from the driving data set recorded by the artificial intelligence thread, separated by the assisted driving evaluation results, and the vehicle control data to be processed covers the previously set constraints for performing intelligent vehicle assisted driving data.

[0007] In an independently implemented embodiment, the determining the control index of the control condition of the reference scene in the previously set constraint condition based on the uninterrupted multiple automobile control events that screen the control condition of the reference scene in the previously set constraint condition includes: determining the automobile control event collection quantification result X in combination with the previously set driving instruction; determining the first X groups of automobile control events of the automobile control data to be processed that are close to the automobile control data to be processed in the driving data set recorded by the artificial intelligence thread, and the last X groups of automobile control events of the automobile control data to be processed that are close to the automobile control data to be processed, and using the first X groups of automobile control events, the automobile control data to be processed, and the last X groups of automobile control events as operation data items to be processed; determining the reference operation data items that cover the control condition of the reference scene in the previously set constraint condition from the operation data items to be processed; and determining the control index of the control condition of the reference scene in the previously set constraint condition in combination with the number of automobile control events included in the reference operation data items.

[0008] In an independently implemented embodiment, the determining the control index of the control condition of the reference scene in the previously set constraint conditions based on the uninterrupted multiple vehicle control events that filter out the control condition of the reference scene in the previously set constraint conditions includes: on the premise that the vehicle control data to be processed that are filtered out in the previously set constraint conditions cover the control condition of the reference scene, based on the number of vehicle control data to be processed that have recently been uninterruptedly filtered out for the control condition of the reference scene, determining the control index of the control condition of the reference scene in the previously set constraint conditions, wherein the vehicle control data to be processed that have recently been uninterruptedly filtered out for the control condition of the reference scene include the vehicle control data to be processed that are filtered out in real time for the control condition of the reference scene, and multiple other uninterrupted vehicle control data to be processed before the vehicle control data to be processed that are filtered out in real time for the control condition of the reference scene; the control condition of the reference scene is covered in the previously set constraint conditions of the other vehicle control data to be processed.

[0009] In an independently implemented embodiment, the control conditions of the reference scene are screened according to the following steps: the vehicle control data to be processed is input into an artificial intelligence thread, the vehicle control event description of the previously set constraint conditions of the vehicle control data to be processed is selected by the artificial intelligence thread, and a reference recognition result of the vehicle control event is output in combination with the vehicle control event description, wherein the reference recognition result is used to indicate whether the control conditions of the reference scene are covered in the previously set constraint conditions.

[0010] In an independently implemented embodiment, after determining the vehicle control data to be processed that covers the previously set constraints for performing intelligent vehicle assisted driving data, it also includes: determining the collected positioning data of no less than one previously set constraint; and combining the positioning data, screening the control conditions of the reference scene in the previously set constraints.

[0011] In a second aspect, a metadata-based intelligent automobile control system is provided, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0012] The metadata-based intelligent automobile control method and system provided in the embodiment of the present application determines the automobile control data to be processed that includes the previously set constraint conditions for intelligent automobile assisted driving data; on the premise that the previously set constraint conditions of the automobile control data to be processed include the control conditions of the reference scene, based on the uninterrupted multiple automobile control events of the control conditions of the reference scene screened out in the previously set constraint conditions, the control index of the control condition of the reference scene in the previously set constraint conditions is determined, and the reference scene includes the previously set control condition scene that matches the automobile status event; on the premise that the control index does not meet the previously set driving instructions, the automobile status event parsing result of the control condition of the screened reference scene is generated. Through the above steps, the automobile can be accurately controlled, so that the precision and accuracy of the automobile intelligent control can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 A flowchart of a metadata-based intelligent vehicle control method provided in an embodiment of the present application.

[0015] Figure 2 A block diagram of a metadata-based intelligent vehicle control device provided in an embodiment of the present application.

[0016] Figure 3 An architectural diagram of a metadata-based intelligent vehicle control system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0018] See also Figure 1 , shows a metadata-based intelligent vehicle control method, which may include the technical solutions described in the following steps 100-300.

[0019] Step 100, determining the vehicle control data to be processed that covers the previously set constraint conditions for performing intelligent vehicle assisted driving data;

[0020] Step 200, on the premise that the previously set constraint conditions of the automobile control data to be processed include the control conditions of the reference scene, based on the uninterrupted multiple automobile control events of the control conditions of the reference scene screened out in the previously set constraint conditions, determine the control index of the control conditions of the reference scene in the previously set constraint conditions, wherein the uninterrupted multiple automobile control events cover the automobile control data to be processed; the reference scene includes the previously set control condition scene that matches the automobile state event;

[0021] Step 300, on the premise that the control index does not satisfy the previously set driving instruction, generate the automobile state item analysis result of the control condition of the screened reference scene.

[0022] It can be understood that when executing the technical solution described in the above steps 100 to 300, the automobile control data to be processed that includes the previously set constraint conditions for the intelligent automobile assisted driving data is determined; on the premise that the previously set constraint conditions of the automobile control data to be processed include the control conditions of the reference scene, based on the uninterrupted multiple automobile control events of the control conditions of the reference scene screened out in the previously set constraint conditions, the control index of the control condition of the reference scene in the previously set constraint conditions is determined, and the reference scene includes the previously set control condition scene that matches the automobile status item; on the premise that the control index does not meet the previously set driving instructions, the automobile status item parsing result of the control condition of the screened reference scene is generated. Through the above steps, the automobile can be accurately controlled, so that the precision and accuracy of the automobile intelligent control can be effectively improved.

[0023] In a possible embodiment, when determining the vehicle control data to be processed that covers the previously set constraints for performing intelligent vehicle assisted driving data, there may be a problem of inaccurate assisted driving evaluation results, making it difficult to accurately determine the vehicle control data to be processed. In order to improve the above technical problems, the step of determining the vehicle control data to be processed that covers the previously set constraints for performing intelligent vehicle assisted driving data described in step 100 may specifically include the content described in step 110.

[0024] Step 110, based on the previously set assisted driving evaluation results, filter out the vehicle control data to be processed from the driving data set recorded by the artificial intelligence thread, and the vehicle control data to be processed covers the previously set constraint conditions for the intelligent vehicle assisted driving data.

[0025] It can be understood that when executing the content described in the above step 110, when determining the vehicle control data to be processed that covers the previously set constraints for the intelligent vehicle assisted driving data, the problem of inaccurate assisted driving evaluation results is improved, so that the vehicle control data to be processed can be accurately determined.

[0026] In a possible embodiment, based on the fact that when the previously set constraint conditions filter out multiple uninterrupted automobile control events to the control conditions of the reference scene, there may be a problem of inaccurate previously set driving instructions, making it difficult to accurately determine the control indicators of the control conditions of the reference scene in the previously set constraint conditions. In order to improve the above technical problems, the step 200 described in the uninterrupted multiple automobile control events that filter out the control conditions of the reference scene in the previously set constraint conditions, determines the control indicators of the control conditions of the reference scene in the previously set constraint conditions, which may specifically include the content described in the following step 210.

[0027] Step 210, in combination with the previously set driving instructions, determine the automobile control event collection quantization result X; determine the first X groups of automobile control events of the automobile control data to be processed that are close to the automobile control data to be processed, and the last X groups of automobile control events that are close to the automobile control data to be processed, in the driving data set recorded by the artificial intelligence thread, and use the first X groups of automobile control events, the automobile control data to be processed, and the last X groups of automobile control events as operation data items to be processed; determine the reference operation data items that cover the control conditions of the reference scene in the previously set constraint conditions from the operation data items to be processed; and determine the control index of the control conditions of the reference scene in the previously set constraint conditions in combination with the number of automobile control events included in the reference operation data items.

[0028] It can be understood that when executing the content described in the above step 210, based on the uninterrupted multiple automobile control events that filter out the control conditions of the reference scene in the previously set constraints, the problem of inaccurate previously set driving instructions is improved, so that the control indicators of the control conditions of the reference scene in the previously set constraints can be accurately determined.

[0029] In a possible embodiment, when a plurality of uninterrupted automobile control events are screened according to the previously set constraint conditions to the control conditions of the reference scene, there may be a problem of inaccurate number of automobile control data to be processed, making it difficult to accurately determine the control index of the control condition of the reference scene in the previously set constraint conditions. In order to improve the above technical problem, the step 200 described in the step of determining the control index of the control condition of the reference scene in the previously set constraint conditions based on a plurality of uninterrupted automobile control events that are screened according to the previously set constraint conditions to the control conditions of the reference scene may specifically include the contents described in the following step a1.

[0030] Step a1, on the premise that the vehicle control data to be processed screened out in the previously set constraint conditions covers the control conditions of the reference scene, based on the number of vehicle control data to be processed that have recently been continuously screened out for the control conditions of the reference scene, determine the control indicators of the control conditions of the reference scene in the previously set constraint conditions, wherein the vehicle control data to be processed that have recently been continuously screened out for the control conditions of the reference scene include the vehicle control data to be processed that have been screened out in real time for the control conditions of the reference scene, and a plurality of other uninterrupted vehicle control data to be processed before the vehicle control data to be processed that have been screened out in real time for the control conditions of the reference scene; the control conditions of the reference scene are covered in the previously set constraint conditions of the other vehicle control data to be processed.

[0031] It can be understood that when executing the content described in the above step a1, when the previously set constraint conditions filter out multiple uninterrupted automobile control events to the control conditions of the reference scene, the problem of inaccurate number of automobile control data to be processed is improved, so that the control indicators of the control conditions of the reference scene in the previously set constraint conditions can be accurately determined.

[0032] In this embodiment, the control conditions of the reference scene are screened according to the following steps: the vehicle control data to be processed is input into an artificial intelligence thread, the artificial intelligence thread selects a vehicle control event description of the previously set constraint conditions of the vehicle control data to be processed, and outputs a reference recognition result of the vehicle control event in combination with the vehicle control event description, wherein the reference recognition result is used to indicate whether the control conditions of the reference scene are covered in the previously set constraint conditions.

[0033] Based on the above foundation, after determining the vehicle control data to be processed that covers the previously set constraints for intelligent vehicle assisted driving data, the following contents may also be included: determining the collected positioning data of no less than one previously set constraint condition; combining the positioning data, screening the control conditions of the reference scene in the previously set constraint condition.

[0034] It can be understood that, through the above steps, the control conditions of the reference scene in the previously set constraint conditions can be accurately screened.

[0035] Based on the above, please refer to Figure 2 , provides a metadata-based intelligent vehicle control device 200, which is applied to a metadata-based intelligent vehicle control system, and the device includes:

[0036] A data determination module 210 is used to determine the vehicle control data to be processed that covers the previously set constraint conditions for performing intelligent vehicle assisted driving data;

[0037] The index determination module 220 is used to determine the control index of the control condition of the reference scene in the previously set constraint conditions based on the uninterrupted multiple vehicle control events of the control condition of the reference scene screened out in the previously set constraint conditions, on the premise that the previously set constraint conditions of the vehicle control data to be processed include the control condition of the reference scene, wherein the uninterrupted multiple vehicle control events cover the vehicle control data to be processed; the reference scene includes the previously set control condition scene that matches the vehicle status event;

[0038] The result analysis module 230 is used to generate a car state item analysis result of the control condition of the screened reference scene under the premise that the control index does not meet the previously set driving instruction.

[0039] Based on the above, please refer to Figure 3 , shows a metadata-based intelligent automobile control system 300, including a processor 310 and a memory 320 that communicate with each other, and the processor 310 is used to read and execute a computer program from the memory 320 to implement the above method.

[0040] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0041] In summary, based on the above scheme, the automobile control data to be processed that covers the previously set constraint conditions for intelligent automobile assisted driving data is determined; on the premise that the previously set constraint conditions of the automobile control data to be processed include the control conditions of the reference scene, based on the uninterrupted multiple automobile control events of the control conditions of the reference scene screened out in the previously set constraint conditions, the control index of the control condition of the reference scene in the previously set constraint conditions is determined, and the reference scene includes the previously set control condition scene that matches the automobile status items; on the premise that the control index does not meet the previously set driving instructions, the automobile status item parsing result of the control condition of the screened reference scene is generated. Through the above steps, the automobile can be accurately controlled, so that the precision and accuracy of the automobile intelligent control can be effectively improved.

[0042] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0043] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other beneficial effects that may be obtained.

[0044] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0045] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0046] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0047] A computer storage medium may include a propagated data signal containing computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0048] The computer program codes required for the operation of each part of the present application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0049] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0050] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0051] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used for the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers allow adaptive changes. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can be changed according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0052] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, instructions, publications, documents, etc., cited in this application are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the content of this application, documents that limit the broadest scope of the claims of this application (currently or later attached to this application) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the attached materials of this application are inconsistent or conflicting with the content described in this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0053] Finally, it should be understood that the embodiments described in this application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, as an example and not a limitation, the alternative configurations of the embodiments of the present application may be considered to be consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0054] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A metadata-based intelligent vehicle control method, characterized in that: The method at least comprises: Determine the vehicle control data to be processed that covers the previously set constraints for intelligent vehicle assisted driving data; On the premise that the previously set constraint conditions of the automobile control data to be processed include the control conditions of the reference scene, based on the uninterrupted multiple automobile control events of the control conditions of the reference scene screened out in the previously set constraint conditions, determining the control index of the control conditions of the reference scene in the previously set constraint conditions, wherein the uninterrupted multiple automobile control events cover the automobile control data to be processed; the reference scene includes the previously set control condition scene that matches the automobile state event; On the premise that the control index does not satisfy the previously set driving instructions, generating a car state item analysis result of the control condition of the screened reference scenario; Wherein, the determining of the vehicle control data to be processed that covers the previously set constraint conditions for performing the intelligent vehicle assisted driving data comprises: according to the previously set assisted driving evaluation result, selecting one vehicle control data to be processed from the driving data set recorded by the artificial intelligence thread at intervals of the assisted driving evaluation result, wherein the vehicle control data to be processed covers the previously set constraint conditions for performing the intelligent vehicle assisted driving data; Wherein, the determining of the control index of the control condition of the reference scene in the previously set constraint conditions based on the uninterrupted multiple vehicle control events that filter the control condition of the reference scene in the previously set constraint conditions includes: In combination with the previously set driving instructions, determine the automobile control event collection quantization result X; determine the first X groups of automobile control events of the automobile control data to be processed that are close to the automobile control data to be processed, and the last X groups of automobile control events of the automobile control data to be processed that are close to the automobile control data to be processed in the driving data set recorded by the artificial intelligence thread, and use the first X groups of automobile control events, the automobile control data to be processed, and the last X groups of automobile control events as operation data items to be processed; determine the reference operation data items that cover the control conditions of the reference scene in the previously set constraint conditions from the operation data items to be processed; and determine the control index of the control conditions of the reference scene in the previously set constraint conditions in combination with the number of automobile control events included in the reference operation data items.

2. The metadata-based intelligent vehicle control method according to claim 1, characterized in that: The determining of the control index of the control condition of the reference scene in the previously set constraint conditions based on the uninterrupted multiple vehicle control events screened from the previously set constraint conditions to the control condition of the reference scene comprises: On the premise that the vehicle control data to be processed screened out in the previously set constraint conditions covers the control conditions of the reference scene, the control indicators of the control conditions of the reference scene in the previously set constraint conditions are determined based on the number of vehicle control data to be processed that have recently been continuously screened out for the control conditions of the reference scene, wherein the vehicle control data to be processed that have recently been continuously screened out for the control conditions of the reference scene include the vehicle control data to be processed that have been screened out in real time for the control conditions of the reference scene, and a plurality of other uninterrupted vehicle control data to be processed before the vehicle control data to be processed that have been screened out in real time for the control conditions of the reference scene; the control conditions of the reference scene are covered in the previously set constraint conditions of the other vehicle control data to be processed.

3. The intelligent vehicle control method based on metadata as claimed in claim 2, characterized in that: The control conditions of the reference scene are screened according to the following steps: the vehicle control data to be processed is input into an artificial intelligence thread, the vehicle control event description of the previously set constraint conditions of the vehicle control data to be processed is selected by the artificial intelligence thread, and a reference recognition result of the vehicle control event is output in combination with the vehicle control event description, wherein the reference recognition result is used to indicate whether the control conditions of the reference scene are covered in the previously set constraint conditions.

4. The metadata-based intelligent vehicle control method according to claim 3, characterized in that: After determining the vehicle control data to be processed that covers the previously set constraints for intelligent vehicle assisted driving data, it also includes: determining the collected positioning data of no less than one previously set constraint condition; combining the positioning data, screening the control conditions of the reference scene in the previously set constraint condition.

5. An intelligent vehicle control system based on metadata, characterized in that: The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 4.

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