Method and device for cockpit python test development and management based on multi-source data visualization, processor and medium thereof

Abstract

The application relates to a method for realizing cockpit Python test development and management based on multi-source data visualization, which comprises the following steps: initializing a test environment for an intelligent cockpit controller; collecting multi-source test data generated in an artificial test process and generating test configuration data; importing the test configuration data into a Python test script written by an external development environment; executing the Python test script through a Python running environment; receiving test running data, performing visual display, realizing test process monitoring and test result analysis. The method, device, processor and computer readable storage medium thereof for realizing cockpit Python test development and management based on multi-source data visualization compensate for the inconvenience of Python in acquiring running data, separate Python test scripts and running data display processing, and adapt to image recognition functions of IC and IVI.

Description

Technical Field

[0001] This invention relates to the field of automotive electronic automated testing technology, and more particularly to the field of intelligent cockpit controllers. Specifically, it refers to a method, apparatus, processor, and computer-readable storage medium for cockpit Python test development and management based on multi-source data visualization. Background Technology

[0002] With the continuous improvement of automotive intelligence, the intelligent cockpit, as the core carrier of human-vehicle interaction, integrates various display devices such as the instrument cluster (IC) and in-vehicle infotainment (IVI). The intelligent cockpit controller undertakes key functions such as display driving, signal processing, and human-machine interaction; the correctness and stability of its software functions directly affect driving safety and user experience. Therefore, the development of intelligent cockpit controllers requires extensive and comprehensive automated testing to verify the correctness of its various functions.

[0003] Currently, testing of intelligent cockpit controllers primarily relies on host computer systems. Existing automated testing systems for intelligent cockpit controllers typically consist of a host computer (PC), a programmable power supply, the intelligent cockpit controller under test (DUT), and auxiliary equipment for screen projection. Most mature testing methods and technologies on the market employ camera-based image recognition. The host computer integrates the control of the programmable power supply, the driving of network tools, and the simulation of vehicle signals into a single software package. Once new functions are added to the cockpit, the host computer cannot meet the testing requirements, or it needs secondary development or the addition of corresponding testing functions, making maintenance extremely difficult. Similarly, replacing the programmable power supply or signal simulation box on the test bench requires secondary development or the addition of corresponding testing functions to the host computer. Existing intelligent cockpit testing software strongly binds the testing API and testing interface, resulting in relatively poor scalability and a high barrier to entry for test development, hindering team collaboration. Because image coordinates need to be manually measured, bus messages need to be manually calculated, and test scripts need to be manually written, the entire test development process demands a high level of technical skill from personnel, making it difficult for non-professional developers to quickly get started. This, to some extent, limited the staffing of the testing team and the efficiency of project progress.

[0004] The second most common framework for smart cockpits on the market is Python. While Python is used as the development and testing language, it has several shortcomings. (1) There is a lack of visual integration between test data and Python script development. The projection interface, bus signal configuration tool, and Python IDE are independent of each other. Test data (whether it is image ROI or CAN / LIN message) cannot be directly imported into the Python test script through visualization. Testers need to switch frequently between multiple tools and manually copy and paste data, which seriously restricts development efficiency. (2) There is a lack of a unified platform for the management and execution of test cases. The writing, refreshing, configuration, and running of Python scripts are scattered in different tools and interfaces, lacking an integrated development management platform. The creation of test cases depends on manual operation and cannot be automatically generated through intuitive methods such as drag and drop. Furthermore, it is difficult for real-time running scripts and test cases to be executed in a coordinated manner.

[0005] In summary, there is an urgent need for a new method and system that can visualize and integrate multi-source test data and directly drive the development and management of Python test scripts, in order to solve the problems of difficulty in acquiring test data, cumbersome configuration, low development efficiency, and high collaboration threshold in existing technologies. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, apparatus, processor and computer-readable storage medium for cockpit Python test development and management based on multi-source data visualization that is simple in structure, low in cost and widely applicable.

[0007] To achieve the above objectives, the present invention provides a method, apparatus, processor, and computer-readable storage medium for cockpit Python test development and management based on multi-source data visualization, as follows: The method for cockpit Python test development and management based on multi-source data visualization using the aforementioned system is characterized by the following steps: (1) Initialize the test environment of the smart cockpit controller through the host computer management platform. The test environment initialization includes controlling the smart cockpit controller to power on, obtaining the display data of the smart cockpit controller, and loading the automotive electronic configuration file corresponding to the smart cockpit controller. (2) Collect multi-source test data generated during manual testing through the host computer management platform, and generate test configuration data based on the multi-source test data; wherein, the manual testing process includes vehicle signal transmission operation and display interface interaction operation, and the multi-source test data includes at least bus signal data and / or vehicle analog signal data, as well as mouse operation data and / or display interface image data; (3) Import the test configuration data into the Python test script written by the external development environment to separate the test data from the test logic code in the Python test script; (4) The Python test script is executed through the Python runtime environment in the host computer management platform. The Python test script performs the test task as an independent process and sends test run data to the host computer management platform through a preset data communication channel. (5) Receive the test operation data through the host computer management platform, and combine the test operation data and the multi-source test data for visualization display, so as to realize test process monitoring and test result analysis.

[0008] Preferably, obtaining the display data of the intelligent cockpit controller in step (1) specifically includes the following steps: The instrument panel IC display stream of the smart cockpit controller is obtained through the ATX protocol, and the in-vehicle infotainment system (IVI) display stream of the smart cockpit controller is obtained through the QNX remote desktop protocol. The dashboard IC display stream and the in-vehicle infotainment system (IVI) display stream are synchronized based on timestamps.

[0009] Preferably, the step (1) of loading the automotive electronic configuration file corresponding to the intelligent cockpit controller specifically includes the following steps: Load the DBC file and / or LDF file, parse the DBC file and / or LDF file, and establish the mapping relationship between bus signal names, physical values, and raw message data.

[0010] Preferably, step (2) of collecting multi-source test data generated during manual testing specifically includes the following steps: Record the click, swipe, and gesture operations performed by the testers on the upper computer screen projection interface, and generate corresponding UI test data based on the displayed interface image data.

[0011] Preferably, the generation of the corresponding UI test data specifically includes the following steps: The target operation area is determined in the upper computer projection interface by using the ROI visualization configuration method, and corresponding coordinate information, image template information and operation trajectory information are generated according to the target operation area.

[0012] Preferably, the method further includes the following steps: The display interface image data before and after the operation corresponding to the target operation area is obtained. The operation results are matched and judged by image recognition and OCR recognition respectively. A two-way check and matching is performed based on the image recognition results and OCR recognition results to determine whether the interactive operation of the display interface is successful.

[0013] Preferably, step (2) of collecting multi-source test data generated during manual testing further includes the following steps: Record the CAN / LIN bus signals and corresponding physical values ​​sent by the tester through the host computer, and generate bus test data; The generated bus test data includes: Based on the bus signal name, physical value, and mapping relationship, dimension conversion, byte stuffing, and checksum calculation are performed to generate the corresponding CAN message data or LIN message data.

[0014] Preferably, after generating the test configuration data in step (2), the interactive operation of the display interface and the bus signal sending operation are synchronized on the time axis by a visual timing editor to generate multimodal test configuration data containing synchronization triggering relationships.

[0015] Preferably, the Python test script sends test run data to the host computer management platform via the UDP protocol; wherein, each Python test script corresponds to an independent multicast address and port, and after the current Python test script releases the corresponding multicast address and port, subsequent Python test scripts use the released multicast address and port.

[0016] Preferably, the host computer management platform supports the parallel execution of multiple test tasks, each test task having an independent multicast address and port, and independently executing the vehicle-mounted real-time simulated message sending task.

[0017] Preferably, the host computer management platform detects changes to the Python test script through a file monitoring mechanism and dynamically updates the test script index based on the detection results.

[0018] Preferably, the method further includes the following steps: By dragging and dropping Python test script files into the test management interface, structured test case entries are automatically generated based on the name of the Python test script file.

[0019] Preferably, the execution of the Python test script in step (4) includes a real-time running mode and a test case running mode, wherein the real-time running mode is used for debugging and executing the Python test script, and the test case running mode is used for batch execution of multiple test tasks. The real-time running mode and the test case running mode run independently and in parallel.

[0020] Preferably, the host computer management platform and the Python runtime environment adopt a loosely coupled architecture; the host computer management platform performs test project management, test script management, test data generation, automotive electronic configuration file parsing, and test task scheduling; the Python runtime environment performs test logic, peripheral device drivers, and real-time data processing during the test process.

[0021] Preferably, the test operation data includes at least one of signal values, image recognition results, execution status, execution time, exception markers, and timestamp data; the host computer management platform displays the test operation data through at least one of waveform graphs, log streams, dashboards, multi-time axis data comparison, and image capture timing alignment.

[0022] The device for cockpit Python test development and management based on multi-source data visualization is characterized by the following features: A processor is configured to execute computer-executable instructions; The memory stores one or more computer-executable instructions, which, when executed by the processor, implement the various steps of the above-described method for cockpit Python test development and management based on multi-source data visualization.

[0023] The processor for cockpit Python test development and management based on multi-source data visualization is characterized in that the processor is configured to execute computer-executable instructions, and when the computer-executable instructions are executed by the processor, the various steps of the above-mentioned method for cockpit Python test development and management based on multi-source data visualization are implemented.

[0024] The main feature of this computer-readable storage medium is that it stores a computer program that can be executed by a processor to implement the various steps of the above-described method for cockpit Python test development and management based on multi-source data visualization.

[0025] This invention employs a method, apparatus, processor, and computer-readable storage medium for cockpit Python test development and management based on multi-source data visualization. It overcomes the inconvenience of Python in acquiring runtime data, separates Python test scripts from runtime data display and processing, and further adapts to the image recognition functions of IC and IVI. It maximizes the convenience of Python script-based test development. Attached Figure Description

[0026] Figure 1 This is a system framework diagram for the development and management of cockpit Python tests based on multi-source data visualization, as described in this invention.

[0027] Figure 2(a) and Figure 2(b) are schematic diagrams of the script running data operation process of the method for realizing cockpit Python test development and management based on multi-source data visualization according to the present invention. The last step of Figure 2(a) is followed by the first step of Figure 2(b).

[0028] Figure 3 This is a schematic diagram of the test development management process of the method for cockpit Python test development and management based on multi-source data visualization according to the present invention. Detailed Implementation

[0029] To more clearly describe the technical content of the present invention, the following description is provided in conjunction with specific embodiments.

[0030] The present invention discloses a method for cockpit Python test development and management based on multi-source data visualization, comprising the following steps: (1) Initialize the test environment of the smart cockpit controller through the host computer management platform. The test environment initialization includes controlling the smart cockpit controller to power on, obtaining the display data of the smart cockpit controller, and loading the automotive electronic configuration file corresponding to the smart cockpit controller. (2) Collect multi-source test data generated during manual testing through the host computer management platform, and generate test configuration data based on the multi-source test data; wherein, the manual testing process includes vehicle signal transmission operation and display interface interaction operation, and the multi-source test data includes at least bus signal data and / or vehicle analog signal data, as well as mouse operation data and / or display interface image data; (3) Import the test configuration data into the Python test script written by the external development environment to separate the test data from the test logic code in the Python test script; (4) The Python test script is executed through the Python runtime environment in the host computer management platform. The Python test script performs the test task as an independent process and sends test run data to the host computer management platform through a preset data communication channel. (5) Receive the test operation data through the host computer management platform, and combine the test operation data and the multi-source test data for visualization display, so as to realize test process monitoring and test result analysis.

[0031] In a preferred embodiment of the present invention, the step (1) of obtaining the display data of the intelligent cockpit controller specifically includes the following steps: The instrument panel IC display stream of the smart cockpit controller is obtained through the ATX protocol, and the in-vehicle infotainment system (IVI) display stream of the smart cockpit controller is obtained through the QNX remote desktop protocol. The dashboard IC display stream and the in-vehicle infotainment system (IVI) display stream are synchronized based on timestamps.

[0032] In a preferred embodiment of the present invention, the step (1) of loading the automotive electronic configuration file corresponding to the intelligent cockpit controller specifically includes the following steps: Load the DBC file and / or LDF file, parse the DBC file and / or LDF file, and establish the mapping relationship between bus signal names, physical values, and raw message data.

[0033] In a preferred embodiment of the present invention, step (2) of collecting multi-source test data generated during manual testing specifically includes the following steps: Record the click, swipe, and gesture operations performed by the testers on the upper computer screen projection interface, and generate corresponding UI test data based on the displayed interface image data.

[0034] In a preferred embodiment of the present invention, the generation of corresponding UI test data specifically includes the following steps: The target operation area is determined in the upper computer projection interface by using the ROI visualization configuration method, and corresponding coordinate information, image template information and operation trajectory information are generated according to the target operation area.

[0035] In a preferred embodiment of the present invention, the method further includes the following steps: The display interface image data before and after the operation corresponding to the target operation area is obtained. The operation results are matched and judged by image recognition and OCR recognition respectively. A two-way check and matching is performed based on the image recognition results and OCR recognition results to determine whether the interactive operation of the display interface is successful.

[0036] In a preferred embodiment of the present invention, step (2) of collecting multi-source test data generated during manual testing further includes the following steps: Record the CAN / LIN bus signals and corresponding physical values ​​sent by the tester through the host computer, and generate bus test data; The generated bus test data includes: Based on the bus signal name, physical value, and mapping relationship, dimension conversion, byte stuffing, and checksum calculation are performed to generate the corresponding CAN message data or LIN message data.

[0037] As a preferred embodiment of the present invention, after generating test configuration data in step (2), the interactive operation of the display interface and the bus signal sending operation are synchronized on the time axis by a visual timing editor to generate multimodal test configuration data containing synchronization triggering relationships.

[0038] In a preferred embodiment of the present invention, the Python test script sends test run data to the host computer management platform via the UDP protocol; wherein, each Python test script corresponds to an independent multicast address and port, and after the current Python test script releases the corresponding multicast address and port, subsequent Python test scripts use the released multicast address and port.

[0039] In a preferred embodiment of the present invention, the host computer management platform supports the parallel execution of multiple test tasks. Each test task corresponds to an independent multicast address and port, and independently executes the vehicle-mounted real-time simulated message sending task.

[0040] In a preferred embodiment of the present invention, the host computer management platform detects changes in the Python test script through a file monitoring mechanism and dynamically updates the test script index based on the detection results.

[0041] In a preferred embodiment of the present invention, the method further includes the following steps: By dragging and dropping Python test script files into the test management interface, structured test case entries are automatically generated based on the name of the Python test script file.

[0042] As a preferred embodiment of the present invention, the execution of the Python test script in step (4) includes a real-time running mode and a test case running mode, wherein the real-time running mode is used for debugging and executing the Python test script, and the test case running mode is used for batch execution of multiple test tasks. The real-time running mode and the test case running mode run independently and in parallel.

[0043] In a preferred embodiment of the present invention, the host computer management platform and the Python runtime environment adopt a loosely coupled architecture; the host computer management platform performs test project management, test script management, test data generation, automotive electronic configuration file parsing, and test task scheduling; the Python runtime environment performs test logic, peripheral device drivers, and real-time data processing during the test process.

[0044] In a preferred embodiment of the present invention, the test operation data includes at least one of signal values, image recognition results, execution status, execution time, anomaly markers, and timestamp data; the host computer management platform displays the test operation data through at least one of waveform graphs, log streams, dashboards, multi-time axis data comparison, and image capture timing alignment.

[0045] The present invention relates to an apparatus for cockpit Python test development and management based on multi-source data visualization, wherein the apparatus includes: A processor is configured to execute computer-executable instructions; The memory stores one or more computer-executable instructions, which, when executed by the processor, implement the various steps of the above-described method for cockpit Python test development and management based on multi-source data visualization.

[0046] The present invention discloses a processor for cockpit Python test development and management based on multi-source data visualization, wherein the processor is configured to execute computer-executable instructions, and when the computer-executable instructions are executed by the processor, the various steps of the above-described method for cockpit Python test development and management based on multi-source data visualization are implemented.

[0047] The computer-readable storage medium of the present invention stores a computer program that can be executed by a processor to implement the various steps of the above-described method for cockpit Python test development and management based on multi-source data visualization.

[0048] This invention belongs to the field of automotive electronic automated testing technology, and relates to a multi-source test data visualization-driven Python test development management system and method for intelligent cockpit controllers. In particular, it relates to a multi-source fusion method of image recognition ROI (Region of Interest) data and CAN / LIN bus signal data based on a host computer platform, as well as a technical solution that drives the automatic generation, configuration, execution and collaborative management of Python test scripts through a visual interactive method.

[0049] The present invention aims to provide a method and apparatus for testing the stability of video streams from controllers with cameras. The method and apparatus are simple in structure, low in cost, and universal. They do not damage the original electronic products and can be easily replicated for system stability testing of different products with cameras.

[0050] This solution adopts a loosely coupled architecture of "host computer management + Python driver" to build a highly adaptable and versatile automated testing system for intelligent cockpit controllers. The host computer focuses on test lifecycle management, script execution scheduling, automotive electronics standard document parsing, and test data generation; Python, as the core carrier of the test logic, is responsible for real-time background task processing, peripheral device drivers, and cockpit function script development. This architecture breaks through the strong coupling limitations of traditional testing tools to specific IDEs or development environments, significantly improving the cross-platform adaptability and scalability of the testing software.

[0051] The platform adopts a three-tier architecture design: The standardized workflow is as follows: 1. Test environment initialization Power supply control: The host computer controls the programmable power supply through the LAN / USB interface to power on the intelligent cockpit controller according to the preset timing (supports 12V / 24V automotive standard power supply curve simulation). System projection: After the controller is powered on, the host computer captures the IC (Instrument Cluster, dashboard) display stream through the ATX (Automotive Test eXtension) protocol and captures the IVI (In-Vehicle Infotainment, vehicle infotainment system) display stream through the QNX (Quick UNIX) remote desktop protocol, and projects the display onto the host computer interface in real time; Configuration file loading: The host computer automatically parses and loads the target controller's DBC (CAN Database) and LDF (LIN Description File) configuration files to establish a mapping relationship library between signal names and physical values.

[0052] 2. Test script development Testers create new development interfaces using a standalone Python IDE and write Python test scripts based on test cases. The host computer provides differentiated data generation support depending on the test type. The generated test data can be directly copied and pasted into the Python script as running data, or exported as a JSON configuration file for the script to load dynamically.

[0053] 3. Script execution and runtime monitoring Script refresh and loading: After the Python script is written, execute the "refresh script" operation on the host computer, and the platform will automatically scan the specified directory and update the test script file index; Test case generation: By dragging and dropping Python files to the host computer test panel, the system automatically generates structured test case entries according to the Python file name, supporting batch import and folder nesting recognition; Multi-mode parallel operation: Real-time running mode: Used for online debugging, supporting breakpoints, single-step execution, and real-time variable monitoring; Test case patterns: Used for regression testing, supporting sequential execution, random execution, and priority-based scheduled execution; The two modes can run in parallel without interfering with each other; Data feedback and visualization: Python scripts transmit real-time running data (signal values, image recognition results, execution status, timestamps, etc.) back to the host computer via UDP protocol, and present it in real time in multiple forms such as waveform graphs, log streams, and dashboards in the visualization panel.

[0054] The main workflow of this testing method is as follows: Figure 3 As shown: The basic workflow is as follows: First, the host computer supplies power to the intelligent cockpit controller via a programmable power supply. After the controller is powered on, it projects the IC and IVI of the cockpit onto the screen through systems such as ATX (Automotive Test eXtension) and QNX (Quick UNIX). It loads the DBC and LDF configuration files of the current controller, opens a new Python development interface in the Python IDE, and develops Python test scripts according to the test cases. If it is image recognition or click and slide, the test data is generated by selecting the area on the host computer's projection interface and copied into the Python script or saved as a JSON configuration file. If it is triggered by CAN / LIN signals, the corresponding trigger value can be set on the host computer to directly generate the corresponding raw message. The raw message can be copied into the Python script as running data or combined with other steps to form a test case running JSON data. After the Python script is written, the script is refreshed on the host computer to refresh the test script file and configure the real-time running Python script. Dragging and dropping Python files will generate test cases according to the Python name. Clicking run allows the real-time running script and the test cases to run simultaneously, and the real-time running data of Python is displayed on the host computer via UDP communication.

[0055] The innovations are as follows: Innovation Point 1: The loosely coupled architecture with separation of responsibilities means that the host computer is only responsible for test script management, execution scheduling, parsing of automotive electronic standard files (DBC / LDF / ARXML), and generation of runtime data; the test logic implementation is entirely handled by Python scripts. This decoupling design of "management" and "implementation" allows test engineers to freely choose their familiar Python IDE and version, avoiding the dependence of traditional tools on closed development environments.

[0056] Innovation Point 2: Pure Runtime Integration Strategy. The host computer only integrates the Python runtime environment (CPython interpreter) and does not have built-in Python script development functionality. Test scripts are entirely imported and executed after external development is completed. This "run-only, no-development" strategy greatly improves the adaptability of Python scripts—supporting all versions of Python 3.x, compatible with any combination of third-party libraries, and allowing virtual environment isolation, fundamentally eliminating the script migration costs caused by IDE version differences.

[0057] Innovation Point 3: Python-Driven Unified Peripheral Device Interface. All peripheral driver devices or test interface devices (CAN / LIN interface cards, IO control modules, relay matrices, data acquisition cards, image acquisition devices, etc.) are encapsulated using Python for secondary development. Through a unified DeviceBase abstract base class, various device drivers are managed and distributed in the form of standard Python packages, significantly improving the universality and portability of the test software across different hardware platforms.

[0058] Innovation Point 4: A Universal Testing Framework for Cross-Domain Expansion. Although this solution takes intelligent cockpit testing as its starting point, its architecture of "host computer management + Python driver + standardized data interface" is highly versatile. By replacing the device driver layer and signal configuration files, it can be smoothly extended to the testing of other automotive electronic controllers such as body control module (BCM), motor controller (MCU), and battery management system (BMS), and can even be extended to a wider range of electronic product testing fields such as industrial automation and medical device electronics.

[0059] Innovation Point 5: Visualized ROI and Zero-Code Test Data Generation. Addressing the most tedious aspect of UI automation testing—coordinate collection—the platform provides a visual ROI selection tool. Testers can directly drag and drop to select target buttons, icons, or areas on the projection screen, and the system automatically calculates the relative / absolute coordinates as the Python runtime configuration file.

[0060] Innovation Point 6: One-click direct conversion platform for DBC / LDF signals to Python code has a built-in automotive electronic standard file parsing engine, which eliminates the need for testers to memorize complex message structures. Testers only need to check target signals (such as VehicleSpeed) in the signal selection panel and input physical values (such as 120 km / h), and the engine automatically completes dimension conversion, byte filling, and checksum calculation, generating the raw message byte sequence that can be directly embedded into Python scripts. Professional bus knowledge is sunk to the tool layer, simplifying the development of Python CAN message sending functions.

[0061] Innovation Point 7: Dual-track parallel mechanism for real-time operation and regression testing The platform break-through supports the parallel operation of "real-time debugging operation" and "batch execution of test cases". Engineers can single-step debug newly developed scripts in the real-time operation window while the test case queue continuously executes regression tests in the background. The two execution environments are isolated from each other with independent data channels, which not only ensures development efficiency but also does not affect the production line test rhythm.

[0062] Innovation Point 8: UDP real-time data closed loop and visual diagnosis Python scripts transmit full-volume data during the test process (signal values, image matching degrees, execution time consumption, exception marks, etc.) back to the host computer in real time through the lightweight UDP protocol. The host computer's visual panel provides functions such as multi-time-axis waveform comparison, time sequence alignment of captured images, and automatic red marking of abnormal nodes, forming a complete data closed loop of "execution - back transmission - analysis - optimization", which significantly shortens the fault location time.

[0063] Innovation Point 9: ATX+QNX dual-system heterogeneous screen projection fusion The platform supports both the ATX protocol (IC screen projection for QNX / Linux underlying systems) and the QNX remote desktop protocol (screen projection for IVI multimedia systems), realizing the simultaneous collection of both the instrument panel and the infotainment system screens. Through a unified timestamp alignment mechanism, the timing accuracy in cross-screen interaction testing is ensured, meeting the test requirements for multi-screen linkage in smart cockpits.

[0064] Innovation Point 10: Unified multi-protocol configuration and collaborative triggering The platform supports unified configuration management of multiple in-vehicle bus protocols such as CAN and LIN. In multi-modal test scenarios, a visual time sequence editor can be used to align UI operations (clicking, swiping) and bus signal transmission on the time axis, realizing zero-code configuration of complex collaborative test cases such as "swiping the central control screen + synchronously sending steering signals".

[0065] Innovation Point 11: Complete Separation of Test Data and Scripts. The platform advocates the concept of "Data-Driven Testing." Through JSON configuration files generated by ROI, message configuration files generated by DBC, and external Excel / CSV test parameter tables, it achieves physical separation between test data and Python scripts. The same script, paired with different configuration files, can adapt to different vehicle model variations, greatly improving the reusability of test assets.

[0066] Innovation Point 12: Hot-swappable Script Dynamic Loading Mechanism. The platform uses file system watcher technology to achieve dynamic script discovery and loading. Test scripts do not require restarting the host computer program; saving takes effect immediately. Drag-and-drop addition, deletion, and renaming operations are reflected in the test case tree in real time.

[0067] Innovation Point 13: Python Virtual Environment Isolation and Dependency Management. The host computer supports the creation of independent Python virtual environments (venv) for different test projects, ensuring that third-party dependencies of each project do not affect each other. The platform automatically parses the requirements.txt file of the script and performs dependency conflict detection, solving the engineering challenge of library version conflicts when multiple projects are running in parallel.

[0068] For the specific implementation scheme of this embodiment, please refer to the relevant descriptions in the above embodiments, which will not be repeated here.

[0069] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0070] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0071] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0072] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution device. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0073] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The corresponding program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0074] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0075] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0076] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0077] This invention employs a method, apparatus, processor, and computer-readable storage medium for cockpit Python test development and management based on multi-source data visualization. It overcomes the inconvenience of Python in acquiring runtime data, separates Python test scripts from runtime data display and processing, and further adapts to the image recognition functions of IC and IVI. It maximizes the convenience of Python script-based test development.

[0078] In this specification, the invention has been described with reference to specific embodiments thereof. However, it will be apparent that various modifications and variations can be made without departing from the spirit and scope of the invention. Therefore, the specification and drawings should be considered illustrative rather than restrictive.

Claims

1. A method for cockpit Python test development and management based on multi-source data visualization, characterized in that, The method includes the following steps: (1) Initialize the test environment of the smart cockpit controller through the host computer management platform. The test environment initialization includes controlling the smart cockpit controller to power on, obtaining the display data of the smart cockpit controller, and loading the automotive electronic configuration file corresponding to the smart cockpit controller. (2) Collect multi-source test data generated during manual testing through the host computer management platform, and generate test configuration data based on the multi-source test data; wherein, the manual testing process includes vehicle signal transmission operation and display interface interaction operation, and the multi-source test data includes at least bus signal data and / or vehicle analog signal data, as well as mouse operation data and / or display interface image data; (3) Import the test configuration data into the Python test script written by the external development environment to separate the test data from the test logic code in the Python test script; (4) The Python test script is executed through the Python runtime environment in the host computer management platform. The Python test script performs the test task as an independent process and sends test run data to the host computer management platform through a preset data communication channel. (5) Receive the test operation data through the host computer management platform, and combine the test operation data and the multi-source test data for visualization display, so as to realize test process monitoring and test result analysis.

2. The method for cockpit Python test development and management based on multi-source data visualization according to claim 1, characterized in that, The step (1) of obtaining the display data of the intelligent cockpit controller specifically includes the following steps: The instrument panel IC display stream of the smart cockpit controller is obtained through the ATX protocol, and the in-vehicle infotainment system (IVI) display stream of the smart cockpit controller is obtained through the QNX remote desktop protocol. The dashboard IC display stream and the in-vehicle infotainment system (IVI) display stream are synchronized based on timestamps.

3. The method for cockpit Python test development and management based on multi-source data visualization according to claim 1, characterized in that, The step (1) of loading the automotive electronic configuration file corresponding to the intelligent cockpit controller specifically includes the following steps: Load the DBC file and / or LDF file, parse the DBC file and / or LDF file, and establish the mapping relationship between bus signal names, physical values, and raw message data.

4. The method for cockpit Python test development and management based on multi-source data visualization according to claim 1, characterized in that, Step (2) involves collecting multi-source test data generated during manual testing, specifically including the following steps: Record the click, swipe, and gesture operations performed by the testers on the upper computer screen projection interface, and generate corresponding UI test data based on the displayed interface image data.

5. The method for cockpit Python test development and management based on multi-source data visualization according to claim 4, characterized in that, The generation of the corresponding UI test data specifically includes the following steps: The target operation area is determined in the upper computer projection interface by using the ROI visualization configuration method, and corresponding coordinate information, image template information and operation trajectory information are generated according to the target operation area.

6. The method for cockpit Python test development and management based on multi-source data visualization according to claim 5, characterized in that, The method further includes the following steps: The display interface image data before and after the operation corresponding to the target operation area is obtained. The operation results are matched and judged by image recognition and OCR recognition respectively. A two-way check and matching is performed based on the image recognition results and OCR recognition results to determine whether the interactive operation of the display interface is successful.

7. The method for cockpit Python test development and management based on multi-source data visualization according to claim 3, characterized in that, Step (2) of collecting multi-source test data generated during manual testing also includes the following steps: Record the CAN / LIN bus signals and corresponding physical values ​​sent by the tester through the host computer, and generate bus test data; The generated bus test data includes: Based on the bus signal name, physical value, and mapping relationship, dimension conversion, byte stuffing, and checksum calculation are performed to generate the corresponding CAN message data or LIN message data.

8. The method for cockpit Python test development and management based on multi-source data visualization according to claim 1, characterized in that, After generating the test configuration data in step (2), the interactive operation of the display interface and the bus signal sending operation are synchronized on the time axis through the visual timing editor to generate multimodal test configuration data containing synchronization triggering relationships.

9. The method for cockpit Python test development and management based on multi-source data visualization according to claim 1, characterized in that, The Python test scripts send test run data to the host computer management platform via the UDP protocol; each Python test script corresponds to an independent multicast address and port, and after the current Python test script releases the corresponding multicast address and port, subsequent Python test scripts use the released multicast address and port.

10. The method for cockpit Python test development and management based on multi-source data visualization according to claim 9, characterized in that, The host computer management platform supports the parallel execution of multiple test tasks. Each test task corresponds to an independent multicast address and port, and independently executes the vehicle-mounted real-time simulated message sending task.

11. The method for cockpit Python test development and management based on multi-source data visualization according to claim 1, characterized in that, The host computer management platform detects changes to Python test scripts through a file monitoring mechanism and dynamically updates the test script index based on the detection results.

12. The method for cockpit Python test development and management based on multi-source data visualization according to claim 11, characterized in that, The method further includes the following steps: By dragging and dropping Python test script files into the test management interface, structured test case entries are automatically generated based on the name of the Python test script file.

13. The method for cockpit Python test development and management based on multi-source data visualization according to claim 1, characterized in that, The execution of the Python test script in step (4) includes a real-time running mode and a test case running mode. The real-time running mode is used for debugging and executing the Python test script, and the test case running mode is used for batch execution of multiple test tasks. The real-time running mode and the test case running mode run independently and in parallel.

14. The method for cockpit Python test development and management based on multi-source data visualization according to claim 1, characterized in that, The host computer management platform and the Python runtime environment adopt a loosely coupled architecture. The host computer management platform performs test project management, test script management, test data generation, automotive electronic configuration file parsing, and test task scheduling. The Python runtime environment performs test logic, peripheral device drivers, and real-time data processing during the test process.

15. The method for cockpit Python test development and management based on multi-source data visualization according to claim 1, characterized in that, The test operation data includes at least one of the following: signal value, image recognition result, execution status, execution time, exception marker, and timestamp data; the host computer management platform displays the test operation data through at least one of the following methods: waveform graph, log stream, dashboard, multi-time axis data comparison, and image capture timing alignment.

16. A device for cockpit Python test development and management based on multi-source data visualization, characterized in that, The device includes: A processor is configured to execute computer-executable instructions; The memory stores one or more computer-executable instructions, which, when executed by the processor, implement the steps of the method for cockpit Python test development and management based on multi-source data visualization as described in any one of claims 1 to 15.

17. A processor for cockpit Python test development and management based on multi-source data visualization, characterized in that, The processor is configured to execute computer-executable instructions, which, when executed by the processor, implement the steps of the method for cockpit Python test development and management based on multi-source data visualization as described in any one of claims 1 to 15.

18. A computer-readable storage medium, characterized in that, It stores a computer program that can be executed by a processor to implement the steps of the method for cockpit Python test development and management based on multi-source data visualization as described in any one of claims 1 to 15.

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