An OTA pressure measurement task and part data efficient deployment method and storage medium
By using the pandas utility library and direct protocol communication to simulate HTTP requests, the problem of low efficiency in OTA stress testing tasks and component data deployment was solved. This enabled fast and automated data uploading and task deployment, significantly improving deployment efficiency and reducing labor costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SAIC GM WULING AUTOMOBILE CO LTD
- Filing Date
- 2026-04-14
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for OTA stress testing and component data deployment suffer from numerous manual operations, high error rates, and low efficiency. They are particularly prone to failure under network latency and GUI operation simulation conditions. Furthermore, the workload for uploading component data is substantial and labor costs are high.
It uses the pandas utility library to process massive amounts of data, simulates HTTP requests through direct protocol communication to bypass the GUI layer and achieve automated deployment. It optimizes operator processing of data streams by using flat data structures and dynamic token capture, generates a target data stream with a uniform format, and communicates directly with the OTA platform API.
It reduces the part data upload time from 30 minutes to 0.8-1.2 seconds, and the single task deployment time from 60 seconds to 0.8-1.2 seconds. It supports batch concurrent deployment, improves the deployment efficiency of OTA upgrade tasks, and reduces labor costs.
Smart Images
Figure CN122420101A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automotive electronic control system testing technology, specifically to a method and storage medium for efficient deployment of OTA stress testing tasks and component data. Background Technology
[0002] Currently, the demand for OTA (Over-The-Air) upgrade testing is enormous. New software for vehicle OTA controllers requires thousands of OTA upgrade tests before release to ensure availability and stability during OTA operation. Existing technologies involve numerous manual deployment tasks and are prone to human error. While Selenium-based automated deployment reduces labor costs, it is limited by GUI (Graphical User Interface) operation simulation mechanisms, requiring complete loading of webpage elements and sequential execution of click actions, resulting in deployment times comparable to manual deployment. More importantly, this solution has inherent flaws: network latency can cause element location timeouts; accidental touches due to external factors can alter the page structure, requiring script re-adaptation or even complete failure.
[0003] Meanwhile, the current workload for uploading component data to support OTA stress testing is substantial, resulting in high labor costs. A single controller can require thousands of data uploads, and manual uploads suffer from time constraints, including unnecessary waiting periods for page refreshes and pop-up confirmations, making them prone to errors and difficult to detect. Summary of the Invention
[0004] The purpose of this invention is to solve the problems existing in the prior art and provide a method for efficient deployment of OTA stress testing tasks and component data. By leveraging the high-efficiency processing capabilities of pandas (an open-source data analysis library based on Python) for massive data, and while meeting the OTA platform interface parameters, the nested JSON and source data stream of the platform interface are compressed into a flat DataFrame (DataFrame is the core data structure in the pandas library, used to store and process two-dimensional tabular data). This allows for rapid processing of multi-dimensional data from the OTA platform and the rapid generation of data with a unified and standardized format.
[0005] This invention is achieved through the following technical solution: A first aspect of the present invention provides a method for automated deployment of OTA stress testing tasks and component data, the method comprising: Step 1: Define and initialize the parameters, including: standardizing the data of the parts to be uploaded to form a source data stream; and defining the task strategy stored in the OTA platform. Step 2: Based on HTTP protocol requests, automatically capture and parse cross-site credentials generated during user login and session maintenance, perform manual authentication, and issue authentication tokens; Step 3: Construct an HTTP request based on the authentication token, communicate directly with the OTA platform API through the direct protocol communication module as the program's public interface, and obtain the cloud data stream returned by the OTA platform; Step 4: Optimize operator processing by using the pandas library to integrate, transform, and reconstruct the source data stream and the cloud data stream to generate the target data stream; Step 5: Based on the target data stream, initiate an operation request to the OTA platform API for uploading part data and deploying stress testing tasks to complete automated deployment; Step 6, End.
[0006] Furthermore, the data standardization process in step 1 includes: applying configurable flattening mapping rules, automatically identifying and expanding key nodes in the multi-layered nested JSON data returned by the OTA platform interface through predefined field path templates; intelligently splitting array elements, and compressing the nested JSON data and the part data stream into a uniformly formatted flattened data structure.
[0007] Furthermore, the cross-site credentials captured in step 2 include a dynamic token and a session identifier; the dynamic token includes the X-CSRF-TOKEN and X-Requested-With request header fields; the session identifier includes the Session ID and the authentication field in the cookie.
[0008] Furthermore, the direct protocol communication in step 3 includes: analyzing and simulating the communication protocol between the front-end and back-end APIs, constructing HTTP requests using the requests library; directly sending requests to specific API endpoints of the OTA platform and processing responses; the specific API endpoints include the part data upload interface and the task creation interface.
[0009] Furthermore, the operation of analyzing and simulating the communication protocol between the front-end and back-end APIs includes: analyzing interface communication, simulating authentication and session maintenance, constructing and sending requests, processing response data, and handling exceptions and retries.
[0010] Furthermore, step 4 includes at least one of the following operations: data merging and association, data transformation and calculation, data screening and filtering, data formatting and reorganization, and abnormal data processing and cleaning.
[0011] Furthermore, the data merging and association involves associating and splicing the source data stream and the cloud data stream to form a dataset containing a complete view of local information and cloud status; the data transformation and calculation involves batch calculating task strategy IDs or upgrade package identifiers that meet the requirements of the OTA platform based on vehicle configuration and parts information; the data filtering and selection involves filtering out a subset of data to be operated on using Boolean indexes; and the data formatting and reorganization involves reorganizing the flattened data structure into a nested structure or grouping it by dimension using functions.
[0012] Furthermore, the batch calculation is performed by dynamically generating or transforming fields according to business rules through vectorized operations.
[0013] Furthermore, the direct protocol communication module bypasses the graphical user interface layer and implements data transmission and modification at the API level.
[0014] A second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the program is executed by a processor, it implements a method for efficiently deploying the OTA stress test task and component data.
[0015] The beneficial effects of this invention are: the direct protocol communication module eliminates GUI operation latency, enabling seamless data transmission from source data to the cloud platform; Pandas optimized operators enable efficient processing of part configuration source data. Single-task deployment time is reduced from approximately 60 seconds manually and 45-50 seconds using the Selenium solution to 0.8-1.2 seconds. It also supports batch concurrent deployment, further improving the efficiency of OTA upgrade task deployment; the upload time for single part and single configuration data is reduced from 30 minutes to 0.8-1.2 seconds, and dynamic adjustment of controller data upload is supported. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of the steps and data flow of the present invention.
[0017] Figure 2 This is a business process diagram of the method of the present invention. Detailed Implementation
[0018] The present invention will now be described in further detail with reference to the accompanying drawings: The core of this invention's method for automating the deployment of OTA stress testing tasks and component data is the use of Python's requests library to simulate HTTP requests and directly communicate with the OTA platform API, avoiding the need for Selenium-based GUI operation simulation (such as page loading and click actions). The basic deployment process of this method includes two phases, such as... Figure 2 As shown.
[0019] Phase 1: Upload Data Upload source data attachments and the user access webpage cookie to the OTA backend. The webpage cookie contains website server information and user login information, etc. Specific operation steps are as follows: Figure 1 As shown, it includes the following two steps: Step 1, Parameter Definition and Initialization: Initialize the data to be uploaded and upgrade the existing task strategy, including: (1) Standardize the data of the parts to be uploaded (such as the names and codes of first-level and second-level suppliers, hardware part numbers, software version numbers, various software refresh parameters, etc.) and store them as source data streams in the corresponding directories. When the automatic deployment program runs, it will retrieve the data from the target directory.
[0020] OTA platform returned interface data often features multi-layered nesting (e.g., nested strategy lists for task information, nested version parameters for part data, etc.), and the structures of different interfaces vary significantly. This invention develops a set of configurable flattened mapping rules that automatically identify and expand key nodes in JSON using predefined field path templates. It also supports intelligent splitting of array elements, compressing the nested JSON and source data stream of the platform interface into a flattened DataFrame structure while meeting the OTA platform interface parameters. Specific operations include: 1. Recursively expand nested objects, expanding nested dictionaries (objects) into multiple columns.
[0021] 2. Handling nested arrays.
[0022] 3. Data cleaning and type conversion to unify data format. Through the above operations, multi-dimensional data from the OTA platform can be processed quickly, and data with a unified and standardized format can be generated.
[0023] (2) Upgrade the task policies stored in the OAT platform, including: upgrade mode, vehicle model to be upgraded, controller version, and preconditions for upgrade.
[0024] Step 2: Parse the authentication flow and capture cross-site credentials. Simultaneously with step 1, this step simulates HTTP protocol request operations. Specifically, it uses an improved crawler framework to parse the authentication stream and employs dynamic token sniffing technology to automatically capture cross-site credentials such as X-CSRF-TOKEN. The parsed objects and content are key credential data generated during user login and session maintenance, such as dynamic tokens (e.g., request header fields like X-CSRF-TOKEN and X-Requested-With) and session identifiers (e.g., Session ID and authentication fields in cookies). The goal is to automate the script's authentication of the OTA platform without manual intervention, enabling subsequent operations such as uploading component data and creating load testing tasks to be executed automatically within a legitimate, authenticated session.
[0025] Phase Two: Perform Automatic Deployment Automated deployment procedures are executed using the pandas utility library's efficient big data processing capabilities, such as... Figure 1 As shown, it includes the following steps: Step 3: The automated deployment program of this invention includes a direct protocol communication module as the program's public interface. Based on the output authentication token, it constructs an HTTP request to communicate with the OTA platform API. Specifically, for user authentication and local data, this invention uses the requests library in Python to simulate HTTP protocol requests, constructing a direct protocol communication module that bypasses the GUI layer, enabling data transmission and modification at the API level. Furthermore, this invention analyzes and simulates the communication protocols between the front-end and back-end APIs, using the requests library to construct HTTP requests, such as analyzing interface communication, simulating authentication and session maintenance, constructing and sending requests, processing response data, and handling exceptions and retry mechanisms. Requests are sent directly to specific API endpoints of the OTA platform (such as parts data upload interfaces, task creation interfaces, etc.). When this request is received and processed by the OTA platform's back-end server, the server returns a response containing the OTA platform's processing result for this operation (such as upload, query, deployment), referred to as the "cloud data stream."
[0026] Step 4, pandas optimizes operator processing The source data stream output from step 1 and the cloud data stream output from step 3 are transmitted to the automated deployment program for parsing to form the target data stream. The target data stream consists of the data to be uploaded and OTA platform response data processed by pandas optimization operators, including user authentication information, task strategies, component information, supplier information, etc.
[0027] Pandas optimization operators utilize the high-performance, vectorized data manipulation methods provided by the pandas library to integrate, transform, filter, and reconstruct source and cloud data streams to generate standardized, batch target data streams that can be directly used by the program for subsequent API communication.
[0028] Specifically, this may include the following processing steps: (1) Data merging and association Using functions such as pd.merge() and pd.concat(), the local source data stream and the cloud data stream obtained from the OTA platform API are associated, compared, or concatenated based on key fields to form a dataset containing a complete view of local information and cloud status.
[0029] (2) Data conversion and calculation Using vectorized operations, the `apply()` function (in conjunction with a custom function), and `map()` replacement, certain fields can be dynamically generated or transformed according to business rules. For example: • Based on vehicle configuration and parts information, batch calculate task strategy IDs or upgrade package identifiers that meet the requirements of the OTA platform; • Convert fields such as timestamps and version numbers into the specific format required by the platform interface; • Calculate and populate new fields based on existing data.
[0030] (3) Data screening and filtering Use Boolean indexes for conditional filtering (df[df['status']=='pending']). Quickly filter out specific subsets of data from massive datasets that require the current operation (such as upload or deployment).
[0031] (4) Data formatting and reorganization Use pivot_table(), groupby(), etc. to reorganize the flattened DataFrame into a nested structure that meets the requirements of a specific API interface (i.e., the reverse process of the "flattening mapping rule" in step 1), or group the data by dimensions such as vehicle type and controller to create stress test tasks in batches.
[0032] (5) Abnormal data processing and cleaning Handling missing values (fillna()), removing duplicate rows (drop_duplicates()), and data type conversion (astype()) ensures that the data to be submitted to the API is clean, complete, and of the correct type, avoiding API call failures due to data quality issues and thus improving the robustness of the entire automation process.
[0033] Step 5: The direct protocol communication module bypasses the GUI layer. Using the final target data stream generated in step 4, initiate the final, specific operation request to the OTA platform API for uploading part data and deploying tasks to complete the entire deployment.
[0034] Step 6, End. Part data upload and upgrade task deployment completed. Specific implementation examples: One embodiment of the present invention involves the initiation of OTA development and testing for a new vehicle model. This model has four different configurations, with 20 modules requiring OTA functionality development. If traditional methods are used for uploading component data and deploying OTA stress test upgrades: Phase 1. Parameter definition and initialization, part data upload It is calculated that a total of 4*20=80 uploads are required (the "4" here refers to 4 different vehicle configurations). The upload time for a single part is about 30 minutes. Therefore, the final time required to complete the part data upload is 80*30=2400 minutes, or 40 hours.
[0036] During program execution, the automated deployment method of this invention is used to standardize the data of the parts to be uploaded and store it as the source data stream in the corresponding directory. Applying the flattened mapping rules of this invention, key nodes in the JSON are automatically identified and expanded using predefined field path templates. Simultaneously, array elements are intelligently split, and the nested JSON and source data stream of the platform interface are compressed into a flattened DataFrame while meeting the OTA platform interface parameters. In this new model OTA development and testing, uploading a single part takes 1 second, resulting in a total time of 80 * 1 = 80 seconds, or 1.33 minutes, for the complete part data upload. Compared to the traditional manual upload method, this represents an efficiency improvement of approximately 1800 times.
[0037] Phase 2. OTA stress test upgrade task deployment The OTA stress test task and component data deployment method designed in this invention are entirely based on automated program execution, with the only manual operation required being to standardize the source data format.
[0038] In this specific deployment, OTA stress testing for a single part needs to be performed 500-2000 times. It is calculated that a total of 4*20=80 rounds of testing are required before the first mass production software approval. At the same time, combined upgrade stress testing is required, and it is believed that at least 20 combinations are required. Therefore, a total of 100 rounds of testing are required before the first mass production software approval. Assuming that the number of cycle upgrades for a single module stress test is 1000, then for a new model, OTA stress testing requires 100*1000=100,000 OTA task deployments.
[0039] The deployment process is as follows: By simulating HTTP protocol requests using the request utility library in Python, a direct protocol communication module is built that bypasses the GUI layer, enabling data transmission and modification at the API level. The source data stream and cloud data stream are transmitted to the automated deployment program for parsing, forming the target data stream, and the upgrade task deployment is completed.
[0040] In this implementation, a single upgrade task deployment takes 60 seconds, resulting in a total OTA stress test time of 1667 hours to complete the new vehicle model controller. In this embodiment, considering that the OTA platform server performance is not affected, the deployment time for a single OTA upgrade task is limited to approximately 1 second. Using the automated deployment method of this invention, the final OTA stress test time for the new vehicle model controller is approximately 28 hours, representing an efficiency improvement of approximately 60 times compared to traditional manual deployment or Selenium automated deployment methods.
[0041] This patented design completely optimizes the OTA stress test task deployment and component data deployment business process, greatly improving the efficiency of task deployment and data upload. At the same time, it takes into account the ease of use and simplicity of the tool, allowing for quick and easy use.
[0042] The above technical solution is only one embodiment of the present invention. For those skilled in the art, based on the principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the technical solutions described in the specific embodiments of the present invention. Therefore, the foregoing description is only a preferred option and is not restrictive.
Claims
1. A method for automated deployment of OTA stress testing tasks and component data, characterized in that, The method includes: Step 1: Define and initialize the parameters, including: standardizing the data of the parts to be uploaded to form a source data stream; and defining the task strategy stored in the OTA platform. Step 2: Based on HTTP protocol requests, automatically capture and parse cross-site credentials generated during user login and session maintenance, perform manual authentication, and issue authentication tokens; Step 3: Construct an HTTP request based on the authentication token, communicate directly with the OTA platform API through the direct protocol communication module as the program's public interface, and obtain the cloud data stream returned by the OTA platform; Step 4: Optimize operator processing by using the pandas library to integrate, transform, and reconstruct the source data stream and the cloud data stream to generate the target data stream; Step 5: Based on the target data stream, initiate an operation request to the OTA platform API for uploading part data and deploying stress testing tasks to complete automated deployment; Step 6, End.
2. The method for automated deployment of OTA stress testing tasks and component data according to claim 1, characterized in that, The data standardization process in step 1 includes: applying configurable flat mapping rules, automatically identifying and expanding key nodes in the multi-layered nested JSON data returned by the OTA platform interface through predefined field path templates; intelligently splitting array elements, and compressing the nested JSON data and the part data stream into a flat data structure with a unified format.
3. The method for automated deployment of OTA stress testing tasks and component data according to claim 2, characterized in that, The cross-site credentials captured in step 2 include a dynamic token and a session identifier; the dynamic token includes the X-CSRF-TOKEN and X-Requested-With request header fields; the session identifier includes the Session ID and the authentication field in the cookie.
4. The method for automated deployment of OTA stress testing tasks and component data according to claim 3, characterized in that, The direct protocol communication operation in step 3 includes: analyzing and simulating the communication protocol between the front-end and back-end APIs, constructing HTTP requests using the requests library; directly sending requests to specific API endpoints of the OTA platform and processing responses; the specific API endpoints include the part data upload interface and the task creation interface.
5. The method for automated deployment of OTA stress testing tasks and component data according to claim 4, characterized in that, The operations of analyzing and simulating the communication protocol between the front-end and back-end APIs include: analyzing interface communication, simulating authentication and session maintenance, constructing and sending requests, processing response data, and exception handling and retries.
6. The method for automated deployment of OTA stress testing tasks and component data according to claim 4 or 5, characterized in that, Step 4 includes at least one of the following operations: data merging and association, data transformation and calculation, data screening and filtering, data formatting and reorganization, and abnormal data processing and cleaning.
7. The method for automated deployment of OTA stress testing tasks and component data according to claim 6, characterized in that, The data merging and association involves associating and splicing the source data stream and the cloud data stream to form a dataset containing a complete view of local information and cloud status. The data transformation and calculation involves batch calculating task strategy IDs or upgrade package identifiers that meet the requirements of the OTA platform based on vehicle configuration and parts information. The data filtering and selection involves filtering the data subsets to be operated on using Boolean indexes. The data formatting and reorganization involves reorganizing the flat data structure into a nested structure or grouping by dimension using functions.
8. The method for automated deployment of OTA stress testing tasks and component data according to claim 7, characterized in that, The batch calculation is performed by dynamically generating or transforming fields according to business rules through vectorized operations.
9. The method for automated deployment of OTA stress testing tasks and component data according to claim 1, characterized in that, The direct protocol communication module bypasses the graphical user interface layer and implements data transmission and modification at the API level.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for efficient deployment of OTA stress testing tasks and component data as described in any one of claims 1 to 9.