CN114647585A - DevOps-based intelligent network connection intelligent driving function cloud simulation test system and method - Google Patents
DevOps-based intelligent network connection intelligent driving function cloud simulation test system and method Download PDFInfo
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Abstract
The invention discloses a DevOps-based intelligent internet vehicle intelligent driving function cloud simulation test system and a method, which comprises a WEB front end and a WEB rear end, application services, third-party services, basic services, a data storage and container arrangement engine subsystem; the WEB front end is used for providing an interactive interface for a user; the WEB back end responds to the operation of a front end user; the third-party service comprises Jenkins service, Gitlab service and ALM service; and the container arrangement engine comprises an image warehouse and a simulation execution node, wherein the image warehouse is used for storing container images of the container-based application development, and images prepared for various users in advance are arranged in the simulation execution node. According to the test system, developers submit software to the project management platform, release the software, automatically construct the platform, integrate and release the software to the mirror image management platform, test and issue a test report, and after analyzing problems, the testers submit a problem list to the defect management platform to close the loop to the developers, so that full process automation is realized.
Description
Technical Field
The invention belongs to the technical field of intelligent networking automobile simulation tests, and particularly relates to a cloud simulation test system and method for intelligent driving functions of an intelligent networking automobile based on a DevOps mode.
Background
The problem can be found quickly and efficiently by developing simulation tests in the autonomous development process of the automatic driving system, the iteration and convergence speed of the whole algorithm are accelerated, the development efficiency is improved on the whole, and the development cost is reduced. Currently, the mainstream simulation test methods include MIL (model in loop), SIL (software in loop), HIL (hardware in loop), VIL (vehicle in loop), DIL (driver in loop), and the like, where SIL can be used for large-scale parallel simulation by virtue of the flexibility, the efficiency of execution, and the convenience of deployment of a pure software environment.
The intelligent driving function of the intelligent networked automobile only needs massive test scenes. Under the condition, the traditional single-machine simulation test has the problems of insufficient computational power and incapability of realizing accelerated test, so that the test period is long and the efficiency is low; by means of the distributed architecture and parallel accelerated computing capability of the simulation of the cloud platform technology, an efficient and safe closed-loop test verification tool is provided for an intelligent driving function through a digital model, a test scene library and a super-computation center, the safety and stability of an intelligent driving product are improved, the scene coverage is increased, the research and development period is shortened, and the method is an effective solution for achieving large-scale simulation scenes of automatic driving.
New business forms are promoted in the global economy explosion, the Internet mobile Internet and other new technologies, and the new business forms further strengthen and promote the urgency of enterprise digital transformation and the importance of the role played by IT in the transformation process. The maturity of new technologies and new research and development engineering practices provide the foundation. Flexible, resilient infrastructure provisioning capabilities such as represented by cloud computing (software defined computing, storage, networking); the architecture practice represented by the micro-service architecture reduces risks for continuous delivery of software and improves flexibility and delivery efficiency; the new software delivery mode represented by Docker simplifies the delivery difficulty and is very suitable for software delivery under a bearer microservice architecture; research and development engineering practices represented by agile development reach a certain maturity, and practice modes such as small-batch and limited work-in-process enable continuous delivery in a streaming manner.
The traditional intelligent driving software development mode and operation and maintenance management system are not suitable for new changes and new requirements (quick response, quick implementation and high quality delivery) under new business forms. With the continuous rising of labor cost in China, intensive development and maintenance systems which rely on a large amount of personnel to invest in testing personnel in the past are overwhelmed; meanwhile, the technical debts accumulated for many years are difficult to adapt to and meet the requirements of the digital transformation and upgrading of enterprises. However, commercial cloud simulation platforms in the market are verification tools, and users need to integrate locally and upload software to be tested manually.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a cloud simulation test system and method for intelligent driving function of an intelligent internet vehicle based on DevOps. The cloud simulation test system for the intelligent driving function of the intelligent networked automobile, provided by the invention, has the advantages that the simulation test task is automatically triggered by the code behavior submitted by developers, and is uniformly managed and scheduled at the cloud end, so that the problem of repeated capacity construction caused by inconsistent tool chains of development and test in the development iteration process of intelligent driving function software of the intelligent networked automobile is solved.
The technical scheme of the invention is realized as follows:
DevOps-based intelligent network connection intelligent driving function cloud simulation test system is characterized in that: the system comprises subsystems such as a WEB front end, a WEB back end, an application service, a third-party service, a basic service and a data storage and container arrangement engine;
the WEB front end runs at the terminal and provides an interactive interface for a user; the system comprises a user login module, a scene management module, a use case management module, a task management module, a result management module and an algorithm management module;
the WEB back end is used for responding to the operation of a front end user and interacting with the database to process various business logics; the system comprises a static configuration data file management module, a simulation task distribution module and a simulation result collection, query and statistics module;
the application service is a service deployed by a user according to business needs; any one, any two, any three or four of CI service, CD service, product service and quality service;
the third-party service comprises any one, any two or three of Jenkins service, Gitlab service and ALM service;
the basic service comprises log service, message service, task scheduling service and monitoring service;
the data storage comprises a short-term storage database, a long-term storage database and a remote dictionary;
the container arrangement engine comprises an image warehouse and a simulation execution node, wherein the image warehouse is used for storing container images of container-based application development, and images prepared in advance for various users are arranged in the simulation execution node.
Therefore, the cloud simulation test system can realize that developers submit software to a project management platform, release the software in a source code management warehouse, integrate and release the software to a mirror image management platform through an automatic construction platform, release the software to the mirror image management platform to carry out simulation test and issue a test report, and finally submit a problem list to a defect management platform to be closed to the developers after the testers analyze the problems, so that the automation of the whole process from software submission to report uploading is realized.
Further: the user login module formulates differentiated access authority according to the group to which the user belongs, and is linked with an external collaborative office system account to acquire more attribute information of the user; the scene management module supports effective import, export, deletion, editing and query of scenes through an interface and a system API, supports the addition of tags to scene files according to user requirements, supports the screening and query in a mode of scene names and scene tags, supports the preview of simulation scenes through the front end of a webpage, comprises static roads and dynamic scenes, supports the triggering of traffic participant behaviors in different modes, comprises the support of absolute position triggering, relative position triggering, time triggering and condition triggering, supports the batch generalization of static scenes through the front end of the webpage, comprises road length, road curvature, lane types and lane widths, and supports the batch generalization of dynamic scenes through the front end of the webpage, comprises the speed, acceleration, initial position and triggering conditions of the traffic participants;
the case management module supports effective import, export, deletion, editing and query of test cases through an interface and a system API, supports establishment of association between the test cases and test scenes, test scripts and user-defined requirements through the interface and the system API, supports batch import of the test cases after local test case compiling, supports test case priority setting, executes the test cases according to the sequence of S, A, B, C during batch simulation, does not execute the test cases when the priority is D, supports grouping of the test cases according to user-defined labels, supports combination of the batch test cases into a test case set, and supports one-key creation of the test cases with specified labels;
the task management module supports creation, suspension, modification and query of test tasks through an interface and a system API, supports selection of algorithms to be tested, test cases and test case sets through the interface and the system API when the test tasks are created, supports dynamic scheduling of nodes to execute the test tasks according to cloud resource use conditions, supports determination of execution sequence according to priorities of the test cases, supports adjustment of test case lists to be executed through the interface and the system API in the execution process of the test tasks, and supports selection of the test case sets to quickly create the test tasks;
after the test is finished, the result management module automatically generates a test report containing important parameters in the simulation process, including vehicle speed, acceleration, vehicle distance and distance collision time, and provides a test result viewing page, wherein the page provides a time domain diagram of a plurality of signals, and the signals are determined by a test scene and signal names related to a test script; the algorithm management module supports software pulled from the source code management warehouse through user interaction interface management, and the functions of adding, deleting, modifying and checking are achieved.
Further: the static configuration data file management module is used for managing additional files of pictures, JavaScript and CSS required by a website; the simulation task allocation module is used for providing test task management and test task execution functions; the simulation result collection, query and statistics module is used for automatically generating a test report for a user to query the report and count problems on line.
And further: the CI service is continuously integrated and consists of a CI engine, a Jenkins engine and a CM2 engine; the CD service is continuously delivered, and the system refers to the fact that a developer delivers the tested software to a simulator through a project management and source code management warehouse; and the product service and the quality service are customized and developed according to the user requirements.
Further: the Jenkins service provides an automatic construction function, and the source code management warehouse is monitored to realize continuous and automatic construction of the software of the tested object; the Gitlab service provides a source code management function, and a developer can trigger a subsequent automatic construction function after delivering the codes; the ALM service provides project management functionality, and developers schedule development plans and submit code at specified times based on project management tasks.
Further: the log service is used for recording all operations of the user in the system; message services are used to send messages among different applications or services; the task scheduling service is used for dynamically scheduling resources such as hardware, storage and the like in the simulation operation process; the monitoring service continuously monitors the system in real time, realizes alarm notification and feeds back the current state of the system in real time.
Further: the short-term storage database is used for storing data frequently accessed by the system and is provided with a high-speed access channel; the long-term storage database needs to store data into storage equipment which can be permanently stored, and the capacity is large; the remote dictionary is used for storing key parameters.
Further: the mirror images in the simulation execution node comprise a real-time task management mirror image, a scene simulation task execution mirror image, a simulation scene rendering environment mirror image, a dynamic model mirror image, a sensor model mirror image and an AI traffic flow model mirror image, wherein the real-time task management mirror image is used for controlling simulation scene switching, the scene simulation task execution mirror image is used for controlling the running and stopping of a simulation scene, the simulation scene rendering environment mirror image is used for rendering a visual image of simulation software, the dynamic model mirror image is used for loading a vehicle dynamic model of a user, the sensor model mirror image is used for loading a sensor model of the user, and the AI traffic flow model mirror image is used for loading an AI traffic flow model of the user.
An intelligent network connection intelligent driving function cloud simulation test method based on DevOps is characterized by comprising the following steps:
1) a developer uploads a code to a project management platform according to a software iteration plan in project management;
2) the project management platform pushes the codes to a corresponding source code management warehouse according to the configuration management rules;
3) the source code management warehouse sends a message to the automatic construction platform, the code inspection platform and the dependence management platform after the new code is released;
4) performing code checking and dependency management;
5) if the code check and the dependency management do not pass, returning to the developer to modify; if the automatic construction is carried out after the passing;
6) after the automatic construction is finished, deploying the mirror image developed by each application to the corresponding node and container through mirror image management;
7) the cloud simulation system executes a test;
8) automatically issuing a simulation test report;
9) the problem is divided into two conditions after system analysis, the first condition is that if the problem is automatically judged through a script, the step of automatically pushing the problem to a defect management platform is skipped; secondly, if manual analysis is needed, jumping to a step of analyzing problems by testers, and then pushing results to a defect management platform;
10) the defect management platform closes the problem to the developer.
Therefore, the cloud simulation test method disclosed by the invention is uniformly managed and dispatched at the cloud end, so that the problem of repeated construction of capacity caused by inconsistency of tool chains of development and test in the development iteration process of intelligent driving function software of the intelligent networked automobile is solved, the integration work of the tested function software and the simulation test platform is realized through the service software aiming at the problems of low continuous test efficiency and high software and simulation tool integration manpower investment, and the function test efficiency is further improved.
In a word, the system and the method for testing the cloud simulation of the intelligent driving function of the intelligent internet vehicle based on the DevOps have the following beneficial effects:
1. the invention can realize that developers submit software to a project management platform, release the software in a source code management warehouse, integrate and release the software to a mirror image management platform through an automatic construction platform, release the software to the mirror image management platform to carry out simulation test and issue a test report, finally submit a problem list to a defect management platform to be closed to the developers after the testers analyze the problems, and realize automation of the whole process from software submission to report uploading.
2. The simulation tasks are uniformly managed and scheduled at the cloud end, so that the problem of capability repeated construction caused by inconsistency of tool chains for development and test in the development and iteration process of intelligent driving function software of the intelligent networked automobile is solved, the integration work of the tested function software and the simulation test platform is realized through the service software aiming at the problems of low continuous test efficiency and high software and simulation tool integration manpower investment, and the function test efficiency is further improved.
Drawings
FIG. 1 is a block diagram of a test system according to an embodiment of the present invention.
Fig. 2 is a flowchart of a testing method according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
as shown in fig. 1 and 2, the cloud simulation test system for intelligent driving function of the intelligent internet vehicle based on the DevOps comprises subsystems such as a WEB front end, a WEB rear end, an application service, a third party service, a basic service, a data storage, a container arrangement engine and the like.
The system comprises a WEB (webpage) front end, a WEB server and a WEB server, wherein the WEB front end runs at a terminal and provides an interactive interface for a user; the system comprises a user login module, a scene management module, a use case management module, a task management module, a result management module and an algorithm management module, wherein the user login module can set up differentiated access authority according to the group to which a user belongs, and can be linked with an external collaborative office system account to acquire more attribute information of the user. The scene management module supports effective import, export, deletion, editing and query of scenes through an interface and a system API, supports addition of tags for scene files according to user requirements, supports screening and query through scene names, scene tags and the like, supports preview of simulation scenes through the front end of a webpage, comprises static roads and dynamic scenes, supports triggering of traffic participant behaviors in different modes, comprises supporting absolute position triggering and relative position triggering, time triggering, condition triggering (traffic light signal, lateral distance from lane line, traffic sign, etc.), support batch generalization of static scenes including but not limited to road length, road curvature, lane type, lane width, etc. by web front end, support batch generalization of dynamic scenes including but not limited to traffic participant speed, acceleration, starting position, triggering condition, etc. The case management module supports effective import, export, deletion, editing and query of test cases through an interface and a system API, supports establishment of association between the test cases and a test scene, a test script and user-defined requirements through the interface and the system API, supports batch import of the test cases after local test case compiling, supports test case priority setting, executes the test cases according to the S, A, B, C sequence during batch simulation, does not execute when the priority is D, supports grouping of the test cases according to user-defined labels, supports combination of the batch test cases into a test case set, and supports one-key creation of the test cases with specified labels. The task management module supports creation, suspension, modification and query of test tasks through an interface and a system API, supports selection of algorithms to be tested, test cases and test case sets through the interface and the system API when the test tasks are created, supports dynamic scheduling of nodes to execute the test tasks according to cloud resource use conditions, supports determination of execution sequence according to priorities of the test cases, supports adjustment of test case lists to be executed through the interface and the system API in the execution process of the test tasks, and supports selection of the test case sets to quickly create the test tasks. And after the test is finished, the result management module automatically generates a test report containing records of important parameters in the simulation process, such as vehicle speed, acceleration, vehicle distance, distance and collision time and the like, and provides a test result viewing page, wherein the page is used for providing a time domain diagram of a plurality of signals, and the signals are determined by the test scene and the signal names related to the test script. An optional signal mapping time domain map should also be provided. The algorithm management module supports management of software pulled from the source code management warehouse through a user interaction interface, and functions of increasing, deleting, modifying, checking and the like are achieved.
A WEB (WEB page) back end which is used for responding to the operation of a front end user and interacting with a database to process various business logics; the system comprises a static configuration data file management module, a simulation task allocation module and a simulation result collection, query and statistics module, wherein the static configuration data file management module is used for managing additional files such as pictures, JavaScript and CSS required by a website. The simulation task allocation module is used for providing test task management and test task execution functions. The simulation result collection, query and statistics module is used for automatically generating a test report for a user to query the report and count problems on line.
The application service is a service deployed by a user according to business needs; any one, any two, any three or four of CI service, CD service, product service and quality service; the CI service is continuously integrated and comprises a CI engine, a Jenkins engine, a CM2 engine and the like. The CD service is delivered continuously, and the system refers to that a developer delivers the tested software to a simulator through a project management and source code management warehouse. And the product service and the quality service are customized and developed according to the user requirements.
The third-party service comprises any one, any two or three of Jenkins service, Gitlab service and ALM service; the Jenkins service provides an automatic construction function, and the source code monitoring management warehouse realizes continuous and automatic construction of the software of the tested object. The Gitlab service provides a source code management function, and a developer can trigger a subsequent automatic construction function after delivering the codes. The ALM service provides project management functionality, and developers schedule development plans and submit code at specified times based on project management tasks.
Basic services including log services, message services, task scheduling services and monitoring services; wherein the logging service is used to record all operations of the user in the system. Message services are used to send messages among different applications or services. And the task scheduling service is used for dynamically scheduling resources such as hardware, storage and the like in the simulation operation process. The monitoring service continuously monitors the system in real time, realizes alarm notification, feeds back the current state of the system in real time, ensures the reliability and safety of the service and ensures the continuous and stable operation of the service.
Data storage, including short-term storage database, long-term storage database, remote dictionary; the short-term storage database is used for storing data frequently accessed by the system and needs to be provided with a high-speed access channel. Long-term storage databases require data to be stored in a storage device that can be permanently stored, and are typically large in capacity. The remote dictionary is used for storing key parameters.
The container arrangement engine comprises a mirror image warehouse and a simulation execution node; wherein the mirror repository is used to store container mirrors for container-based application development. The real-time task management mirror image can control the switching of the simulation scenes, the scene simulation task execution mirror image is used for controlling the running and the stopping of the simulation scenes, the simulation scene rendering environment mirror image is used for rendering a visual image of simulation software, the dynamic model mirror image is used for loading a vehicle dynamic model of a user, the sensor model mirror image is used for loading a sensor model of the user, and the AI traffic flow model mirror image is used for loading an AI traffic flow model of the user.
The subsystems of the WEB front end, the WEB rear end, the application service, the third-party service, the basic service, the data storage, the container arrangement engine and the like form the intelligent Internet vehicle intelligent driving function cloud simulation test system based on the DevOps, can realize that developers submit software to a project management platform, release the software in a source code management warehouse, integrate and release the software to a mirror image management platform through an automatic construction platform, release the software to the mirror image management platform to carry out simulation test and issue a test report, finally submit a problem list to a defect management platform to close a loop to the developers after the testers analyze the problems, and realize the automation of the whole process from software submission to report uploading.
The invention relates to a cloud simulation test method for intelligent driving functions of an intelligent networked vehicle based on DevOps, which comprises the following steps:
step 1: and uploading codes to a project management platform by a developer according to a software iteration plan in the project management, wherein the configuration management rule is carried by the system, and the source code management warehouse corresponds to the Gitlab service module.
Step 2: and the project management platform pushes the codes to a corresponding source code management warehouse according to the configuration management rules. The configuration management rule is self-contained in the system, and the source code management warehouse corresponds to the front Gitlab service module.
And step 3: and the source code management warehouse sends a message to the automatic construction platform, the code inspection platform and the dependence management platform after the new code is released. The automatic construction platform, the code inspection platform and the dependence management platform correspond to Jenkins modules.
And 4, step 4: code checking and dependency management is performed.
And 5: if the code check and the dependency management do not pass, returning to the developer to modify; if the automatic construction is carried out after passing.
Step 6: and after the automatic construction is finished, deploying the mirror image developed by each application to the corresponding node and container through mirror image management. Here, a node is a node for simulation execution, and a container is a module running inside the node.
And 7: the cloud simulation system performs the test.
And 8: and automatically issuing a simulation test report.
And step 9: the problems are divided into two types after system analysis, namely, the step 10.1 is skipped to through automatic judgment of scripts; a jump to step 10.2 requiring manual analysis.
Step 10.1: automatically pushing the problem to a defect management platform.
Step 10.2: and the tester analyzes the problems and then pushes the result to the defect management platform.
Step 10: the defect management platform closes the problem to the developer.
The simulation tasks are uniformly managed and scheduled at the cloud end, so that the problem of capability repeated construction caused by inconsistency of tool chains for development and test in the development and iteration process of intelligent driving function software of the intelligent networked automobile is solved, the integration work of the tested function software and the simulation test platform is realized through the service software aiming at the problems of low continuous test efficiency and high software and simulation tool integration manpower investment, and the function test efficiency is further improved.
Finally, it should be noted that the above-mentioned examples of the present invention are only examples for illustrating the present invention, and are not intended to limit the embodiments of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, it will be apparent to those skilled in the art that other variations and modifications can be made based on the above description. Not all embodiments are exhaustive. All obvious changes and modifications of the present invention are within the scope of the present invention.
Claims (9)
1. DevOps-based intelligent network connection intelligent driving function cloud simulation test system is characterized in that: the system comprises subsystems such as a WEB front end, a WEB back end, an application service, a third-party service, a basic service and a data storage and container arrangement engine;
the WEB front end runs at the terminal and provides an interactive interface for a user; the system comprises a user login module, a scene management module, a use case management module, a task management module, a result management module and an algorithm management module;
the WEB back end is used for responding to the operation of a front end user and interacting with the database to process various business logics; the system comprises a static configuration data file management module, a simulation task distribution module and a simulation result collection, query and statistics module;
the application service is a service deployed by a user according to business needs; any one, any two, any three or four of CI service, CD service, product service and quality service;
the third-party service comprises any one, any two or three of Jenkins service, Gitlab service and ALM service;
the basic service comprises log service, message service, task scheduling service and monitoring service;
the data storage comprises a short-term storage database, a long-term storage database and a remote dictionary;
the container arrangement engine comprises an image warehouse and simulation execution nodes, wherein the image warehouse is used for storing container images of container-based application development, and images prepared for various users in advance are arranged in the simulation execution nodes.
2. The cloud simulation test system for intelligent driving function of internet vehicle based on DevOps as claimed in claim 1, wherein: the user login module formulates differentiated access authority according to the group to which the user belongs, and is linked with an external collaborative office system account to acquire more attribute information of the user;
the scene management module supports effective import, export, deletion, editing and query of scenes through an interface and a system API, supports the addition of tags to scene files according to user requirements, supports the screening and query in a mode of scene names and scene tags, supports the preview of simulation scenes through the front end of a webpage, comprises static roads and dynamic scenes, supports the triggering of traffic participant behaviors in different modes, comprises the support of absolute position triggering, relative position triggering, time triggering and condition triggering, supports the batch generalization of static scenes through the front end of the webpage, comprises road length, road curvature, lane types and lane widths, and supports the batch generalization of dynamic scenes through the front end of the webpage, comprises the speed, acceleration, initial position and triggering conditions of the traffic participants;
the case management module supports effective import, export, deletion, editing and query of test cases through an interface and a system API, supports establishment of association between the test cases and test scenes, test scripts and user-defined requirements through the interface and the system API, supports batch import of the test cases after local test case compiling, supports test case priority setting, executes the test cases according to the sequence of S, A, B, C during batch simulation, does not execute the test cases when the priority is D, supports grouping of the test cases according to user-defined labels, supports combination of the batch test cases into a test case set, and supports one-key creation of the test cases with specified labels;
the task management module supports creation, suspension, modification and query of test tasks through an interface and a system API, supports selection of algorithms to be tested, test cases and test case sets through the interface and the system API when the test tasks are created, supports dynamic scheduling of nodes to execute the test tasks according to cloud resource use conditions, supports determination of execution sequence according to priorities of the test cases, supports adjustment of test case lists to be executed through the interface and the system API in the execution process of the test tasks, and supports selection of the test case sets to quickly create the test tasks;
after the test is finished, the result management module automatically generates a test report containing important parameters in the simulation process, including vehicle speed, acceleration, distance between vehicles and collision time, and provides a test result viewing page, wherein the page provides a time domain diagram of a plurality of signals, and the signals are determined by a test scene and signal names related to a test script;
the algorithm management module supports software pulled from the source code management warehouse through user interaction interface management, and the functions of adding, deleting, modifying and checking are achieved.
3. The cloud simulation test system for intelligent driving function of internet vehicle based on DevOps as claimed in claim 1, wherein: the static configuration data file management module is used for managing additional files of pictures, JavaScript and CSS required by a website; the simulation task allocation module is used for providing test task management and test task execution functions; the simulation result collection, query and statistics module is used for automatically generating a test report for a user to query the report and count problems on line.
4. The cloud simulation test system for intelligent driving function of internet vehicle based on DevOps as claimed in claim 1, wherein: the CI service is continuously integrated and consists of a CI engine, a Jenkins engine and a CM2 engine; the CD service is continuously delivered, and the system refers to that a developer delivers the tested software to a simulator through a project management and source code management warehouse; and the product service and the quality service are customized and developed according to the user requirements.
5. The cloud simulation test system for intelligent driving function of intelligent networked vehicle based on DevOps as claimed in any one of claims 1-4, wherein: the Jenkins service provides an automatic construction function, and the source code management warehouse is monitored to realize continuous and automatic construction of the software of the tested object; the Gitlab service provides a source code management function, and a developer can trigger a subsequent automatic construction function after delivering the codes; the ALM service provides project management functionality, and developers schedule development plans and submit code at specified times based on project management tasks.
6. The cloud simulation test system for intelligent driving function of intelligent networked vehicle based on DevOps as claimed in any one of claims 1-4, wherein: the log service is used for recording all operations of the user in the system; message services are used to send messages among different applications or services; the task scheduling service is used for dynamically scheduling resources such as hardware, storage and the like in the simulation operation process; the monitoring service continuously monitors the system in real time, realizes alarm notification and feeds back the current state of the system in real time.
7. The cloud simulation test system for intelligent driving function of intelligent networked vehicle based on DevOps as claimed in any one of claims 1-4, wherein: the short-term storage database is used for storing data frequently accessed by the system and is provided with a high-speed access channel; the long-term storage database needs to store data into storage equipment which can be permanently stored, and the capacity is large; the remote dictionary is used for storing key parameters.
8. The cloud simulation test system for intelligent driving function of intelligent networked vehicle based on DevOps as claimed in any one of claims 1-4, wherein: the mirror images in the simulation execution node comprise a real-time task management mirror image, a scene simulation task execution mirror image, a simulation scene rendering environment mirror image, a dynamic model mirror image, a sensor model mirror image and an AI traffic flow model mirror image, wherein the real-time task management mirror image is used for controlling simulation scene switching, the scene simulation task execution mirror image is used for controlling the running and stopping of a simulation scene, the simulation scene rendering environment mirror image is used for rendering a visual image of simulation software, the dynamic model mirror image is used for loading a vehicle dynamic model of a user, the sensor model mirror image is used for loading a sensor model of the user, and the AI traffic flow model mirror image is used for loading an AI traffic flow model of the user.
9. An intelligent network connection intelligent driving function cloud simulation test method based on DevOps is characterized by comprising the following steps:
1) a developer uploads codes to a project management platform according to a software iteration plan in the project management;
2) the project management platform pushes the codes to a corresponding source code management warehouse according to the configuration management rule;
3) the source code management warehouse sends a message to the automatic construction platform, the code inspection platform and the dependency management platform after the new code is issued;
4) performing code checking and dependency management;
5) if the code check and the dependency management do not pass, returning to the developer to modify; if the automatic construction is carried out after the passing;
6) after the automatic construction is finished, deploying the mirror image developed by each application to the corresponding node and container through mirror image management;
7) the cloud simulation system executes a test;
8) automatically issuing a simulation test report;
9) the problem is divided into two conditions after system analysis, the first condition is that if the problem is automatically judged through a script, the step of automatically pushing the problem to a defect management platform is skipped; secondly, if manual analysis is needed, jumping to a step of analyzing problems by testers, and then pushing results to a defect management platform;
10) the defect management platform closes the problem to the developer.
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