System and method for processing automatic driving data

Abstract

The invention discloses a system and a method for processing automatic driving data, and relates to the technical field of automatic driving. A specific embodiment of the method comprises the following steps of: receiving a user instruction by utilizing an intelligent agent, analyzing a user intention, converting the user intention into an operation type of automatic driving data, and according to the operation type, in a registered application program interface (API), executing the automatic driving data; screening out and sending a plurality of target APIs for processing the automatic driving operation data, an execution sequence of the plurality of target APIs and an operation type of the automatic driving data; receiving an operation type of the automatic driving data by using the data driving platform, obtaining the automatic driving operation data from the database, and generating a processing result for the automatic driving operation data; according to the embodiment, the utilization efficiency of the automatic driving data can be improved, and the operation complexity of the automatic driving data is reduced.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to a system and method for processing autonomous driving data. Background Technology

[0002] Autonomous driving refers to the use of advanced sensors, artificial intelligence, high-precision maps, and computing platforms to enable cars to automatically and safely perceive the environment, make decisions, and execute driving operations without the active intervention of a human driver, ultimately delivering passengers or goods from the origin to the destination.

[0003] In the process of realizing this invention, the inventors discovered that the prior art has at least the following problems: due to the large amount of autonomous driving data and the complexity of driving scenarios, the use of autonomous driving data is complicated and inefficient. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a system, method, device, and computer-readable medium for processing autonomous driving data, thereby improving the utilization efficiency of autonomous driving data and reducing the operational complexity of autonomous driving data.

[0005] A system for processing autonomous driving data includes an intelligent agent and a data-driven platform;

[0006] The intelligent agent is configured to receive user instructions in a dialog box, analyze user intent based on the user instructions and historical instructions and results, convert the user intent into an operation type for autonomous driving data, and, based on the operation type of the autonomous driving data, filter and send multiple target APIs for processing the autonomous driving operation data, the execution order of the multiple target APIs, and the operation type of the autonomous driving data from the registered application programming interface (API); receive the processing result of the autonomous driving operation data, and output the processing result of the autonomous driving operation data in the dialog box.

[0007] The data-driven platform is used to retrieve autonomous driving operation data from the database according to the operation type of the autonomous driving data, call the target API according to the execution order of the target API, process the autonomous driving operation data to generate a processing result for the autonomous driving operation data, and send the processing result of the autonomous driving operation data.

[0008] According to a second aspect of the present invention, a method for processing autonomous driving data is provided, applied to a data-driven platform, comprising:

[0009] Based on the operation type of the autonomous driving data, autonomous driving operation data is obtained from the database, and multiple target APIs for processing the autonomous driving operation data and the execution order of the multiple target APIs are obtained from the registered application programming interface (API).

[0010] The target APIs are invoked according to their execution order to process the autonomous driving operation data and generate processing results for the autonomous driving operation data.

[0011] The method further includes:

[0012] The MCP server and MCP client receive the output parameters of the processing results of the autonomous driving operation data, and output the processing results of the autonomous driving operation data using the output parameters.

[0013] The types of operations performed on the autonomous driving data include data simulation or model training;

[0014] The step of obtaining autonomous driving operation data from the database includes:

[0015] Retrieve autonomous driving operation data from the database for data simulation or autonomous driving operation data for model training;

[0016] The types of operations performed on the autonomous driving data include software development, data evaluation, or data analysis.

[0017] The step of obtaining autonomous driving operation data from the database includes:

[0018] Based on the operation identifiers in software development, data evaluation, or data analysis, search the database for dependent data identifiers that are related to the operation type;

[0019] Extract the autonomous driving operation data corresponding to the dependent data identifier.

[0020] The types of operations performed on the autonomous driving data include data simulation or model training;

[0021] The method further includes:

[0022] The execution order of the multiple target APIs matches multiple tasks of the data simulation model or the data training model to be verified;

[0023] Based on the execution order of the multiple target APIs, the autonomous driving operation data is processed by multiple tasks of the data simulation model or data training model to be verified, which are sequentially called through the target APIs. The data simulation model or data training model to be verified is then verified, and the data simulation model or data training model is optimized based on the verification results.

[0024] The types of operations performed on the autonomous driving data include data simulation or model training;

[0025] The autonomous driving operation data includes: autonomous driving source data and target output results corresponding to each of the target APIs;

[0026] The process of processing the autonomous driving operation data to generate a processing result for the autonomous driving operation data includes:

[0027] According to the execution order of the multiple target APIs, the autonomous driving source data is input into the first target API, the output result of the previous target API is used as the input data of the next target API, and the API processing result of each target API is recorded.

[0028] The API processing result of each target API is compared with the target output result of the corresponding target API included in the autonomous driving operation data, and the comparison result is used as the processing result for the autonomous driving operation data.

[0029] The types of operations performed on the autonomous driving data include data evaluation;

[0030] The step of obtaining the autonomous driving operation data from the database includes:

[0031] Based on the evaluation object and driving scenario targeted by the data evaluation, a standardized dataset is obtained from the database. The standardized dataset includes: test data and standardized results corresponding to each target API.

[0032] The process of processing the autonomous driving operation data to generate a processing result for the autonomous driving operation data includes:

[0033] According to the execution order of the multiple target APIs, the test data is input into the first target API, the output result of the previous target API is used as the input data of the next target API, and the API processing result of each target API is recorded.

[0034] The API processing result of each target API is compared with the standardized result of the target API, and the comparison result is used as the processing result for the autonomous driving operation data.

[0035] The types of operations performed on the autonomous driving data include software development;

[0036] The step of obtaining the autonomous driving operation data from the database includes:

[0037] Based on the software development version identifier and development environment, retrieve the corresponding development code from the database;

[0038] The process of processing the autonomous driving operation data to generate a processing result for the autonomous driving operation data includes:

[0039] According to the execution order of the multiple target APIs, the development code is input into the first target API, and the software development processing result is output from the last target API in the execution order. The software development processing result is used as the processing result of the autonomous driving operation data.

[0040] The types of operations performed on the autonomous driving data include data analysis;

[0041] The step of obtaining the autonomous driving operation data from the database includes:

[0042] Based on the object of data analysis, the autonomous driving operation data of the object of analysis is obtained from the database;

[0043] The process of processing the autonomous driving operation data to generate a processing result for the autonomous driving operation data includes:

[0044] According to the execution order of the target APIs, the autonomous driving operation data of the analysis object is input into the first target API, and the last target API in the execution order outputs data anomalies, which are then used as the processing result of the autonomous driving operation data.

[0045] The step of invoking the target API according to the execution order of the target API includes:

[0046] The software package of the target API is obtained according to the target API identifier in the execution order of the target API, and the software package of the target API is stored in a preset directory;

[0047] The target API is invoked in the preset directory according to the execution order of the APIs and the package of the target API.

[0048] According to a third aspect of the present invention, an electronic device for processing autonomous driving data is provided, comprising:

[0049] One or more processors;

[0050] Storage device for storing one or more programs.

[0051] When the one or more programs are executed by the one or more processors, the one or more processors perform the methods described above.

[0052] According to a fourth aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0053] One embodiment of the above invention has the following advantages or beneficial effects: The intelligent agent receives user instructions in a dialog box and filters out multiple target APIs for processing autonomous driving operation data based on the operation type of the autonomous driving data. The data-driven platform uses the target APIs to process the autonomous driving operation data and can directly obtain the processing results. The intelligent agent outputs the processing results of the autonomous driving operation data in the dialog box. Because it can extract autonomous driving operation data and determine target APIs independently based on user instructions through interaction with the intelligent agent, the efficiency of utilizing autonomous driving operation data is improved, while the operational complexity of autonomous driving data is reduced.

[0054] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0055] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0056] Figure 1 This is a schematic diagram of the main structure of a system for processing autonomous driving data according to an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of the main structure of another system for processing autonomous driving data according to an embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of the main structure of another system for processing autonomous driving data according to an embodiment of the present invention;

[0059] Figure 4 This is a schematic diagram of the main flow of a method for processing autonomous driving data according to an embodiment of the present invention;

[0060] Figure 5 This is a schematic diagram of the structure of a computer system suitable for implementing terminal devices or servers of the present invention. Detailed Implementation

[0061] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0062] To improve the efficiency of autonomous driving data utilization, the following technical solutions from the embodiments of the present invention can be adopted.

[0063] See Figure 1 , Figure 1 This is a schematic diagram of the main structure of a system for processing autonomous driving data according to an embodiment of the present invention.

[0064] The system for processing autonomous driving data includes an intelligent agent 101 and a data-driven platform 102.

[0065] Intelligent agent 101 is used to receive user instructions in a dialog box, analyze user intent based on the user instructions and historical instructions and results, and convert the user intent into an operation type for autonomous driving data. Based on the operation type of autonomous driving data, it selects and sends multiple target APIs for processing autonomous driving operation data, the execution order of the multiple target APIs, and the operation type of autonomous driving data from the registered application programming interface (API). It also receives the processing result of autonomous driving operation data and outputs the processing result of autonomous driving operation data in a dialog box.

[0066] The data-driven platform 102 is used to retrieve autonomous driving operation data from the database according to the operation type of the autonomous driving data, call the target API according to the execution order of the target API, process the autonomous driving operation data to generate processing results for the autonomous driving operation data, and send the processing results of the autonomous driving operation data.

[0067] In one embodiment of the present invention, an intelligent agent 101 is used to process autonomous driving data. The intelligent agent 101 can be trained using autonomous driving training data. It should be noted that the autonomous driving training data includes real autonomous driving data uploaded to the database during vehicle operation, and may also include data improved based on real autonomous driving data.

[0068] As an example, agent 101 was trained using prompt words, utility functions, and autonomous driving training data.

[0069] When analyzing user intent, agent 101 can rely not only on received user commands but also on historical commands and results. Historical commands are the user's past instructions, and historical results are the processing outcomes corresponding to those commands. Analyzing user intent by combining historical commands and results significantly improves the accuracy of intent recognition.

[0070] The data-driven platform 102 includes a database. The database stores autonomous driving operation data. The data-driven platform 102 includes a registered application programming interface (API). Through interaction between the intelligent agent 101 and the data-driven platform 102, the processing results of the autonomous driving operation data are output to the dialog box of the intelligent agent 101.

[0071] In one embodiment of the present invention, multiple target APIs for processing autonomous driving operation data are selected from registered APIs. These multiple target APIs need to process the autonomous driving operation data sequentially. Therefore, while selecting multiple target APIs, the execution order of these target APIs can also be determined. As an example, multiple target APIs are selected from the registered APIs, namely a first target API, a second target API, and a third target API, and the execution order of the target APIs is determined as follows: first target API; third target API; second target API. In another embodiment of the present invention, an API model can be used in the intelligent agent to select target APIs and their execution order from the APIs registered in the data-driven platform.

[0072] Specifically, the intelligent agent trains an API model based on autonomous driving training data, the target API of the autonomous driving training data, and the execution order of multiple autonomous driving training data target APIs.

[0073] The autonomous driving operation data is input into the API model, which then filters out the target API and its execution order from the registered APIs.

[0074] See Figure 2 , Figure 2 This is a schematic diagram of the main structure of another system for processing autonomous driving data according to an embodiment of the present invention.

[0075] Figure 2 The components are: intelligent agent 201, MCP client 202, MCP server 203, and data-driven platform 204. MCP stands for Model Context Protocol.

[0076] The intelligent agent 201 interacts with the data-driven platform 204 through the MCP client 202 and the MCP server 203.

[0077] As an example, agent 201 sends the operation type of autonomous driving data, multiple target APIs for processing autonomous driving operation data, and the execution order of multiple target APIs to data-driven platform 204 through MCP client 202 and MCP server 203.

[0078] The data-driven platform 204 sends the processing results of autonomous driving operation data to the intelligent agent 201 through the MCP server 203 and the MCP client 202.

[0079] See Figure 3 , Figure 3 This is a schematic diagram of the main structure of another system for processing autonomous driving data according to an embodiment of the present invention.

[0080] The system for processing autonomous driving data includes intelligent agent 301, MCP client 302, MCP server 303, and data-driven platform 304.

[0081] The data-driven platform 304 includes several APIs, such as API 3041, API 3042, and API 3043. These APIs can be called within the data-driven platform 304 to process autonomous driving data.

[0082] See Figure 4 , Figure 4 This is a schematic diagram of the main flow of a method for processing autonomous driving data according to an embodiment of the present invention. The processing of autonomous driving data is achieved through a data-driven platform. Figure 4 As shown in 400, the specific steps include:

[0083] S401. Based on the operation type of the autonomous driving data, retrieve the autonomous driving operation data from the database, and from the registered APIs, retrieve multiple target APIs for processing autonomous driving operation data and the execution order of the multiple target APIs.

[0084] In an embodiment of the present invention, the user sends user instructions through an intelligent agent, and through the MCP client and the MCP server, sends multiple target APIs for processing autonomous driving operation data and the execution order of multiple target APIs to the driving data driving platform.

[0085] Application Programming Interfaces (APIs) are registered in the data-driven platform, allowing users to call these registered APIs. The database stores autonomous driving operation data. The APIs can serve as abstract processing interfaces for different types of operations and purposes. Figure 4 The execution entity for each step is the data-driven platform.

[0086] Specifically, based on the operation type of the autonomous driving data, autonomous driving operation data is retrieved from the database, and multiple target APIs for processing autonomous driving operation data are obtained from the registered APIs.

[0087] In one embodiment of the present invention, an intelligent agent, an MCP client, and an MCP server can be used to interact with the user.

[0088] Specifically, the intelligent agent receives and parses user instructions, obtaining from the user instructions the operation type for autonomous driving data, multiple target APIs for processing autonomous driving operation data, the execution order of the multiple target APIs, and output parameters related to the data processing result; it also receives the operation type, multiple target APIs for processing autonomous driving operation data, the execution order of the multiple target APIs, and output parameters forwarded by the MCP client. Furthermore, it outputs the processing result of the autonomous driving operation data using the output parameters.

[0089] As an example, the types of operations involving autonomous driving data include one or more of the following: data simulation, model training, software development, data evaluation, and data analysis. All of these operations require the use of autonomous driving data. Given the large volume and diverse types of autonomous driving data, manual processing is unlikely to improve processing efficiency.

[0090] Output parameters related to the data processing results are those parameters that are output to the user after the autonomous driving data has been processed. As an example, output parameters related to the data processing results include the data processing result output format, the data processing result output path, and the data processing result return fields.

[0091] By leveraging intelligent agents, the difficulty of interacting with data-driven platforms can be reduced, thereby improving the efficiency of processing autonomous driving data.

[0092] The MCP client connects to one or more MCP servers, enabling interaction with multiple MCP servers through an intelligent agent. The MCP client receives and forwards the operation type of autonomous driving data, multiple target APIs for processing autonomous driving operation data, the execution order of multiple target APIs, and the output parameters of the data processing results.

[0093] In embodiments of the present invention, the API is registered on a data-driven platform, which stores identifiers for various APIs, such as data acquisition API identifiers, data storage API identifiers, model training API identifiers, and data testing API identifiers.

[0094] The data-driven platform filters out multiple target APIs from the registered APIs based on the identifiers of the target APIs that process autonomous driving operation data.

[0095] In embodiments of the present invention, different types of autonomous driving data result in different autonomous driving operation data. Autonomous driving operation data can be retrieved from a database based on the type of autonomous driving data being processed.

[0096] In one embodiment of the present invention, the operation type of autonomous driving data includes data simulation or model training.

[0097] Data simulation uses data to verify the performance of autonomous driving systems or algorithms. It is mainly used to cover extreme scenarios, dangerous scenarios, and long-tail problems. Model training uses data to train a better vehicle control model.

[0098] For data simulation or model training, autonomous driving operation data for either simulation or training is retrieved from the database. This data includes not only the data itself but also data annotations. Utilizing this combined data and annotations enables a data closed loop for simulation and training, thereby gradually reducing errors in the autonomous driving system or algorithm and improving the accuracy of the model's output.

[0099] In one embodiment of the present invention, the operation type of autonomous driving data includes software development, data evaluation, or data analysis.

[0100] Software development includes the continuous integration and deployment of autonomous driving software. Data evaluation includes quantitatively assessing and comparing the performance of different algorithms and model versions. Data analysis involves identifying problems in autonomous driving from massive amounts of autonomous driving data, thereby adjusting corresponding algorithms and models.

[0101] The server searches the database for dependent data identifiers that are related to the operation based on the operation identifiers from software development, data evaluation, or data analysis. It then extracts the autonomous driving operation data corresponding to the dependent data identifiers.

[0102] As an example, software development needs to be executed based on the autonomous driving operation data after data simulation is completed. Therefore, software development and data simulation are dependent on each other. As another example, data evaluation needs to be performed after model training to analyze the model's performance. It is clear that data evaluation is dependent on model training.

[0103] Dependency data identifiers are used to identify the output autonomous driving operation data for operation types with dependencies. Autonomous driving operation data can be extracted from the database based on these dependency data identifiers. The extracted autonomous driving operation data is then used as the autonomous driving operation data for the corresponding operation type.

[0104] In the above embodiments, based on the dependency relationship between operation types, the corresponding autonomous driving operation data is obtained, further improving the continuity of processing autonomous driving data.

[0105] S402. Call the target API according to the execution order of the target API, process the autonomous driving operation data to generate the processing result for the autonomous driving operation data.

[0106] In embodiments of the present invention, the data-driven platform processes autonomous driving operation data based on target APIs. Specifically, the data-driven platform invokes the target APIs according to their execution order.

[0107] In one embodiment of the present invention, the target API can be invoked in the following manner: The data-driven platform obtains the target API's software package according to the target API identifier in the execution order of the target APIs, and stores the target API's software package in a preset directory. The target API is then invoked in the preset directory according to the API execution order and the target API's software package.

[0108] Calling multiple target APIs within a preset directory allows the preset directory to function as an independent environment, ensuring that APIs are called in the order they are executed, thus improving call stability.

[0109] As an example, in the execution order of the target APIs inputting autonomous driving operation data, the first target API corresponds to the identifier of the target API, and the last target API in the execution order outputs the processing result of the autonomous driving operation data. Since the order in which the target APIs process the data is already determined, the above data processing process does not require intervention and can be executed sequentially.

[0110] In one embodiment of the present invention, the operation type of autonomous driving data includes data simulation or model training, and the processing results of autonomous driving operation data can be generated in the following ways.

[0111] The autonomous driving operation data includes: autonomous driving source data and target output results corresponding to each of the target APIs.

[0112] According to the execution order of multiple target APIs, the autonomous driving source data is input into the first target API, the output result of the previous target API is used as the input data of the next target API, and the API processing result of each target API is recorded.

[0113] The API processing result of each target API is compared with the target output result of the corresponding target API included in the autonomous driving operation data, and the comparison result is used as the processing result for the autonomous driving operation data.

[0114] As an example, the execution order of multiple target APIs includes: a first target API; a second target API; and a third target API. Autonomous driving source data is input into the first target API. The first target API outputs its processing result; the processing result of the first API is input into the second target API, which outputs its processing result; the processing result of the second API is input into the third target API, which outputs its processing result.

[0115] The target output results of the corresponding target APIs included in the autonomous driving operation data are: the target output result of the first API; the target output result of the second API; and the target output result of the third API.

[0116] Compare the processing result of the first API with the target output result of the first API; compare the processing result of the second API with the target output result of the second API; compare the processing result of the third API with the target output result of the third API. Use these three comparison results as the processing results for the autonomous driving operation data.

[0117] Since the processing results of autonomous driving operation data include comparison results of the target API, parameters can be adjusted for data simulation or model training to optimize data simulation or model training.

[0118] In one embodiment of the present invention, the operation type of autonomous driving data includes data simulation or model training.

[0119] Considering that data simulation utilizes a data simulation model and data training utilizes a data training model, a target API can be used to optimize either the data simulation model or the data training model.

[0120] Specifically, multiple target APIs and their execution order are matched to multiple tasks of the data simulation model or data training model to be validated. As an example, multiple target APIs and their execution order are matched to multiple tasks whose purpose is to validate the data simulation model or data training model.

[0121] Based on the execution order of the multiple target APIs, multiple tasks of the data simulation model or data training model to be verified are sequentially called through the target APIs to process autonomous driving operation data and verify the data simulation model or data training model to be verified. The data simulation model or data training model to be verified outputs the verification result. Then, the data simulation model or data training model is optimized using the above verification result.

[0122] In one embodiment of the present invention, the operation type of autonomous driving data includes data evaluation.

[0123] Considering the high correlation between the evaluation objects and driving scenarios and autonomous driving operation data, standardized datasets can be obtained from the database based on the evaluation objects and driving scenarios used in the data evaluation. The standardized datasets include: test data and standardized results corresponding to each target API. As an example, the evaluation objects include models and algorithms. The evaluation driving scenarios include: the vehicle in front decelerating, the vehicle changing lanes, and the vehicle making a U-turn.

[0124] The use of standardized datasets helps improve the objectivity of data evaluation and reduces the impact of data quality on the results of autonomous driving operation data processing.

[0125] The following methods can be used to generate the processing results of autonomous driving operation data:

[0126] Following the execution order of multiple target APIs, the test data is input into the first target API, the output of the previous target API is used as the input data for the next target API, and the API processing result of each target API is recorded.

[0127] The API processing result of each target API is compared with the standardized result of the target API, and the comparison result is used as the processing result for autonomous driving operation data.

[0128] By comparing the results with the standardized results of the target API, data evaluation of the target API is achieved to determine the applicability of each target API.

[0129] In one embodiment of the present invention, the operation type of autonomous driving data includes software development.

[0130] Software development focuses on the reproducibility of data. Specifically, based on the software version identifier and development environment, the corresponding development code is retrieved from the database. Understandably, different software version identifiers or development environments will result in different retrieved development code. For example, the development code might be used to control the opening and closing of vehicle windows. As another example, the development code might be used for over-the-air (OTA) updates to the vehicle. Following the execution order of multiple target APIs, the development code is input into the first target API, and the software processing result is output from the last target API in the execution order. This software processing result is then used as the processing result for autonomous driving operation data.

[0131] In the above embodiments, errors in the software development process can be proactively discovered through the corresponding development code, thereby improving software quality.

[0132] In one embodiment of the present invention, the operation type for autonomous driving data includes data analysis.

[0133] Depending on the object of data analysis, the autonomous driving operation data retrieved from the database will differ depending on the object being analyzed. For example, the objects being analyzed include sensors, the vehicle braking system, and the vehicle battery system.

[0134] Based on the execution order of the target APIs, the autonomous driving operation data of the analysis object is input into the first target API. The last target API in the execution order outputs data anomalies, which are then used as the processing result of the autonomous driving operation data. As an example, the autonomous driving operation data includes sensor data collected by multiple sensors. Cross-validation is performed using data from multiple sensors to locate data anomalies.

[0135] In the above embodiments, based on the autonomous driving operation data obtained from the analysis object, multiple target APIs are called to analyze the data and send data anomaly points.

[0136] In the embodiments of the present invention described above, the intelligent agent receives user instructions in a dialog box. Based on the operation type of the autonomous driving data, multiple target APIs for processing the autonomous driving operation data are selected. The data-driven platform uses the target APIs to process the autonomous driving operation data and can directly obtain the processing results. The intelligent agent outputs the processing results of the autonomous driving operation data in the dialog box. Because it can automatically extract autonomous driving operation data and determine target APIs based on user instructions through interaction with the intelligent agent, the efficiency of utilizing autonomous driving operation data is improved, while the operational complexity of autonomous driving data is reduced.

[0137] The following is for reference. Figure 5 It shows a schematic diagram of the structure of a computer system 500 suitable for implementing a terminal device of the present invention. Figure 5 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0138] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the system 500. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0139] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 510 as needed so that computer programs read from it can be installed into storage section 508 as needed.

[0140] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs the functions defined above in the system of this invention.

[0141] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0142] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0143] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include:

[0144] Based on the operation type of the autonomous driving data, autonomous driving operation data is obtained from the database, and multiple target APIs for processing the autonomous driving operation data and the execution order of the multiple target APIs are obtained from the registered application programming interface (API).

[0145] The target APIs are invoked according to their execution order to process the autonomous driving operation data and generate processing results for the autonomous driving operation data.

[0146] According to the technical solution of this embodiment of the invention, the intelligent agent receives user instructions in a dialog box and filters out multiple target APIs for processing autonomous driving operation data based on the operation type of the autonomous driving data. The data-driven platform uses the target APIs to process the autonomous driving operation data and can directly obtain the processing results. The intelligent agent outputs the processing results of the autonomous driving operation data in the dialog box. Because it can extract autonomous driving operation data and determine target APIs independently based on user instructions through interaction with the intelligent agent, the efficiency of utilizing autonomous driving operation data is improved, while reducing the operational complexity of autonomous driving data.

[0147] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention. It should be noted that the acquisition, storage, and application of user personal information involved in the technical solutions of this disclosure comply with relevant laws and regulations and do not violate public order and good morals.

Claims

1. A system for processing autonomous driving data, characterized in that, Including intelligent agents and data-driven platforms; The intelligent agent is used to receive user instructions in a dialog box, analyze user intent based on the user instructions and historical instructions and results, convert the user intent into an operation type for autonomous driving data, and, based on the operation type of the autonomous driving data, filter and send multiple target APIs for processing the autonomous driving operation data, the execution order of the multiple target APIs, and the operation type of the autonomous driving data from the registered application programming interface (API). Receive the processing results of the autonomous driving operation data and output the processing results of the autonomous driving operation data in the dialog box; The data-driven platform is used to retrieve autonomous driving operation data from the database according to the operation type of the autonomous driving data, call the target API according to the execution order of the target API, process the autonomous driving operation data to generate a processing result for the autonomous driving operation data, and send the processing result of the autonomous driving operation data.

2. A method for processing autonomous driving data, characterized in that, Applied to data-driven platforms, including: Based on the operation type of the autonomous driving data, autonomous driving operation data is obtained from the database, and multiple target APIs for processing the autonomous driving operation data and the execution order of the multiple target APIs are obtained from the registered application programming interface (API). The target APIs are invoked according to their execution order to process the autonomous driving operation data and generate processing results for the autonomous driving operation data.

3. The method for processing autonomous driving data according to claim 2, characterized in that, The method further includes: The MCP server and MCP client receive the output parameters of the processing results of the autonomous driving operation data, and output the processing results of the autonomous driving operation data using the output parameters.

4. The method for processing autonomous driving data according to claim 2, characterized in that, The types of operations performed on the autonomous driving data include data simulation or model training; The step of obtaining autonomous driving operation data from the database includes: Retrieve autonomous driving operation data from the database for data simulation or autonomous driving operation data for model training; The types of operations performed on the autonomous driving data include software development, data evaluation, or data analysis. The step of obtaining autonomous driving operation data from the database includes: Based on the operation identifiers in software development, data evaluation, or data analysis, search the database for dependent data identifiers that are related to the operation type; Extract the autonomous driving operation data corresponding to the dependent data identifier.

5. The method for processing autonomous driving data according to claim 2, characterized in that, The types of operations performed on the autonomous driving data include data simulation or model training; The method further includes: The execution order of the multiple target APIs matches multiple tasks of the data simulation model or the data training model to be verified; Based on the execution order of the multiple target APIs, the autonomous driving operation data is processed by multiple tasks of the data simulation model or data training model to be verified, which are sequentially called through the target APIs. The data simulation model or data training model to be verified is then verified, and the data simulation model or data training model is optimized based on the verification results.

6. The method for processing autonomous driving data according to claim 2, characterized in that, The types of operations performed on the autonomous driving data include data simulation or model training; The autonomous driving operation data includes: autonomous driving source data and target output results corresponding to each of the target APIs; The process of processing the autonomous driving operation data to generate a processing result for the autonomous driving operation data includes: According to the execution order of the multiple target APIs, the autonomous driving source data is input into the first target API, the output result of the previous target API is used as the input data of the next target API, and the API processing result of each target API is recorded. The API processing result of each target API is compared with the target output result of the corresponding target API included in the autonomous driving operation data, and the comparison result is used as the processing result for the autonomous driving operation data.

7. The method for processing autonomous driving data according to claim 2, characterized in that, The types of operations performed on the autonomous driving data include data evaluation; The step of obtaining the autonomous driving operation data from the database includes: Based on the evaluation object and driving scenario targeted by the data evaluation, a standardized dataset is obtained from the database. The standardized dataset includes: test data and standardized results corresponding to each target API. The process of processing the autonomous driving operation data to generate a processing result for the autonomous driving operation data includes: According to the execution order of the multiple target APIs, the test data is input into the first target API, the output result of the previous target API is used as the input data of the next target API, and the API processing result of each target API is recorded. The API processing result of each target API is compared with the standardized result of the target API, and the comparison result is used as the processing result for the autonomous driving operation data.

8. The method for processing autonomous driving data according to claim 2, characterized in that, The types of operations performed on the autonomous driving data include software development; The step of obtaining the autonomous driving operation data from the database includes: Based on the software development version identifier and development environment, retrieve the corresponding development code from the database; The process of processing the autonomous driving operation data to generate a processing result for the autonomous driving operation data includes: According to the execution order of the multiple target APIs, the development code is input into the first target API, and the software development processing result is output from the last target API in the execution order. The software development processing result is used as the processing result of the autonomous driving operation data.

9. The method for processing autonomous driving data according to claim 2, characterized in that, The types of operations performed on the autonomous driving data include data analysis; The step of obtaining the autonomous driving operation data from the database includes: Based on the object of data analysis, the autonomous driving operation data of the object of analysis is obtained from the database; The process of processing the autonomous driving operation data to generate a processing result for the autonomous driving operation data includes: According to the execution order of the target APIs, the autonomous driving operation data of the analysis object is input into the first target API, and the last target API in the execution order outputs data anomalies, which are then used as the processing result of the autonomous driving operation data.

10. The method for processing autonomous driving data according to claim 2, characterized in that, The step of invoking the target API according to the execution order of the target API includes: The software package of the target API is obtained according to the target API identifier in the execution order of the target API, and the software package of the target API is stored in a preset directory; The target API is invoked in the preset directory according to the execution order of the APIs and the package of the target API.

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