Intelligent vehicle DTS data collection and query system

Abstract

An intelligent vehicle DTS data collection and query system, comprising a cloud platform. The cloud platform is configured with a data collection port for acquiring vehicle DTS data by means of the data collection port. The cloud platform performs identification processing on the collected DTS data, and stores the data in a database, wherein the database is a structured database.

Description

A smart car DTS data acquisition and query system

[0001] This application claims priority to Chinese Patent Application No. 202510372732.1, filed on March 27, 2025, entitled "An Intelligent Vehicle DTS Data Acquisition and Query System", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of automotive DTS data processing, and in particular to an intelligent automotive DTS data acquisition and query system. Background Technology

[0003] To meet consumers' evolving aesthetic demands and attract more new car buyers, OEMs must continuously launch models with innovative designs, ensuring that the designs conform to the Dimensional Technical Specification (DTS). This necessitates detailed DTS data collection for each prototype vehicle to effectively learn from competitors' and domestically developed models.

[0004] Typically, most vehicle models contain 150-200 cross-sections, with 1600-2000 DTS (Difference Tolerance) measurement points for both interior and exterior trim. Currently, various methods exist for DTS data acquisition, including manual measuring tools (such as surface difference tables, clearance gauges, and laser measuring instruments) and handheld and robotic arm-based automated clearance and surface difference measurement equipment. In manual measurement, data acquisition personnel record the measurement results one by one, and then summarize all data into an Excel spreadsheet file. Each prototype vehicle corresponds to one Excel file, and each file contains multiple sheets recording the actual DTS measurement results for different cross-sections. In automated measurement, cross-section images must first be uploaded to the equipment supplier's proprietary software. The information for each measurement point, including its name, location, nominal value, and tolerance, must be manually set. Then, the automated measuring equipment measures the actual DTS values ​​of each prototype vehicle, and the results are exported as a fixed template file.

[0005] However, the current workflow has several problems. First, data acquisition personnel need a basic understanding of the vehicle's cross-sections and measurement points, as well as relevant measurement experience, which is a significant challenge for beginners. Second, when designing a new vehicle model, it is necessary to understand all existing models, making it tedious to search for the required information from numerous Excel files, thus reducing work efficiency. When analyzing multiple vehicle models or performing trend analysis on a specific model, the entire process becomes complex and error-prone. Summary of the Invention

[0006] This application provides an intelligent vehicle DTS data acquisition and query system. This system, through the establishment of a cloud platform, enables various data acquisition and query methods, thereby improving the efficiency of data acquisition and query. The technical solution is as follows:

[0007] According to one aspect of this application, an intelligent vehicle DTS data acquisition and query system is provided, the system including a cloud platform, the cloud platform being configured with a data acquisition port;

[0008] The cloud platform is used to acquire DTS data through the data acquisition port;

[0009] The cloud platform is used to identify and process the collected DTS data, and store the processed DTS data in a database, which is a structured database.

[0010] In one optional embodiment, the cloud platform includes a data acquisition module;

[0011] The data acquisition module is used to connect to the data acquisition device through the data acquisition interface. The data acquisition device supports manual and automatic acquisition of DTS data and sends the DTS data to the data acquisition module through the data acquisition interface. The data acquisition module parses and processes the uploaded DTS data to obtain storable DTS data and stores it in the database.

[0012] In one optional embodiment, the data acquisition device is connected to the data acquisition interface via a network. The data acquisition interface is a network service interface. The data acquisition device is used to upload the DTS data to the cloud platform and send it to the database through the data acquisition interface. The data acquisition device includes at least one of the following: a web terminal device, a mobile terminal device, and an automatic testing device. The data acquisition device is used to realize data input and acquisition.

[0013] In one optional embodiment, the web-based device and / or the mobile device inputs the DTS data through a pre-developed templated webpage; or, the web-based device and / or the mobile device inputs the DTS data by importing an Excel file, and the data acquisition module is used to parse the DTS data in the Excel file.

[0014] In one optional embodiment, the cloud platform further includes a data processing module for calculating evaluation indicators based on the collected DTS data, the evaluation indicators being used to evaluate the vehicle design and manufacturing level.

[0015] In one optional embodiment, the evaluation index includes at least one of the following: DTS-related instruction index, DTS trend index of the same vehicle model, and DTS comparison index of different vehicle models.

[0016] In one optional embodiment, the cloud platform further includes a permission management module, which is used to manage user accounts connected to the cloud platform and to restrict and manage user data collection, uploading, modification, and query operations.

[0017] In one optional embodiment, the cloud platform is equipped with a data query module;

[0018] The data query module is used to perform data analysis and processing on the received query request data when a user account with query permissions sends a query request to the data query module after accessing the cloud platform, obtain the corresponding query results, and feed them back to the user account.

[0019] In one optional embodiment, the data query module includes at least one of the following units: a hierarchical retrieval unit, an image search unit, and an interactive question and answer unit. The hierarchical retrieval unit is used to perform data query and positioning based on a hierarchical positioning method with multiple dimensions according to the user's query request. The multiple dimensions include at least two of the following dimensions: brand, vehicle series, vehicle model, region, and cross-section.

[0020] The image search unit is used to query the DTS data of similar images stored in the database of the cloud platform according to the image uploaded by the user's query request and the image similarity.

[0021] The interactive question-and-answer unit is used to perform text queries based on keywords in the user's query request, analyze the request intent, and provide corresponding results.

[0022] In an optional embodiment, the data query module further includes a data export unit, which is used to export the queried DTS data according to a preset file format based on the user query request. The beneficial effects of the technical solution provided by this application include at least:

[0023] By building a cloud platform, multiple data collection and query methods are enabled, improving the efficiency of data collection and querying. Data is stored in a structured database for easy subsequent querying and processing. The platform supports multiple data upload methods during queries and monitors and manages users who collect and upload data based on access control, enhancing the reliability and traceability of data collection. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 is a functional block diagram of the intelligent vehicle DTS data acquisition and query system of this application;

[0026] Figure 2 is a schematic diagram of the data acquisition lock module of this application;

[0027] Figure 3 is a schematic diagram of the image search principle corresponding to the query function of this application;

[0028] Figure 4 is a schematic diagram of the voice interactive query principle of this application.

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. Detailed Implementation

[0030] The specific implementation of this application will be further described in detail below with reference to the accompanying drawings and the description of the preferred embodiments.

[0031] This application proposes an "Intelligent Vehicle DTS Data Acquisition and Query System". This system is a multi-terminal application, including a web (web page) terminal and a mobile terminal, and integrates multiple data acquisition and query methods, aiming to improve the efficiency of data acquisition and data query.

[0032] During manual testing, users can input data one by one on the web interface or import data from an Excel file, or input data while measuring using a mobile device. During automated testing, after preliminary preparation, the system can communicate with the automated measurement equipment via the network, start automatic measurement with a single click, and directly input the data into the database. The system uses a structured database to store data, facilitating subsequent querying and analysis.

[0033] The system also provides multiple query methods, greatly facilitating users' information retrieval, including:

[0034] (1) Hierarchical query: perform hierarchical queries based on brand, series, model, region, cross section, etc. to quickly locate the required data.

[0035] (2) Image search: Users can quickly find data with similar cross sections by uploading cross section images.

[0036] (3) Interactive Q&A: Users can ask and answer questions based on text to obtain relevant information.

[0037] The launch of the intelligent vehicle DTS data acquisition and query system aims to build and manage a knowledge base for vehicle DTS information, effectively improve data processing efficiency, lower the work experience threshold, and provide more scientific and systematic support for vehicle design.

[0038] As shown in Figure 1, an intelligent vehicle DTS data acquisition and query system includes a cloud platform. The cloud platform is equipped with a data acquisition port, through which it acquires DTS data. The cloud platform processes and identifies the acquired DTS data, then stores the processed DTS data in a structured database. The cloud platform is built on a server, and the data acquisition port is developed through software. The acquired DTS data is processed and stored in the database. The cloud platform is connected to a network. Authorized user accounts access the cloud platform and query the database through the query service module to retrieve the desired data.

[0039] The cloud platform includes a data acquisition module, which connects to data acquisition devices via a data acquisition interface. These devices support both manual and automatic DTS data acquisition and send the data to the acquisition module through the data acquisition interface. The acquisition module parses and processes the uploaded DTS data to obtain storable DTS data, which is then stored in the database. Data acquisition devices include, but are not limited to, mobile phones, computers, and professional equipment. When using a mobile phone or computer, DTS data is manually entered by opening a pre-defined webpage. When using professional equipment, data is collected and uploaded to the cloud platform according to the equipment's instructions. The cloud platform includes servers, a database, and the data acquisition module. The data acquisition module collects, processes, and transforms the data before storing it in the database via the server. The cloud platform is built on a server and uses software service programs or software function modules to implement the corresponding data acquisition, storage, and query functions.

[0040] The data acquisition device connects to a data acquisition interface via a network. This data acquisition interface is a network service interface. The data acquisition device uploads DTS data to a cloud platform and sends it to a database through this interface. The data acquisition device includes at least one of the following: a web-based device, a mobile device, or an automated testing device. The data acquisition device is used to input and collect data. The web-based device and / or mobile device inputs DTS data through a pre-developed templated webpage; or, the web-based device and / or mobile device inputs DTS data by importing an Excel file. The data acquisition module is used to parse the DTS data in the Excel file. If the DTS data is in an Excel file, the corresponding DTS data can be obtained by parsing it using the data acquisition module.

[0041] The cloud platform includes a data processing module that calculates evaluation metrics based on the collected DTS data. These metrics are used to assess the design and manufacturing quality of vehicles. The evaluation metrics include at least one of the following: DTS-related instruction metrics, DTS trend metrics for the same vehicle model, and DTS comparison metrics for different vehicle models. Users can use these metrics to evaluate vehicles and obtain corresponding DTS data for future vehicle upgrades and modifications.

[0042] DTS-related instruction indicators are key quality evaluation parameters calculated by the system based on the collected DTS data, such as CP, CPK, 6σ, symmetry, and parallelism. These indicators are used to objectively evaluate the degree of conformity between the actual measured values ​​of the dimensional technical specifications of various vehicle components and the design standards, thereby reflecting the accuracy and consistency of the manufacturing process.

[0043] The DTS trend index for the same vehicle model is obtained by longitudinally analyzing the DTS data of different batches or different periods of a certain vehicle model. It identifies the changing patterns and development trends of the measurement point data, helps users discover potential problems in design improvement or manufacturing deviations, and provides data support for continuous optimization.

[0044] The DTS comparison index for different car models combines the DTS data of multiple car models for horizontal comparison and analysis. By quantifying the dimensional differences, pass rates, stability and other indicators of each car model at the same measurement point, it helps users quickly identify the advantages and disadvantages of different car models and provides a basis for design reference and market competition analysis.

[0045] Since the cloud platform stores DTS data in a database, not everyone has the right to access, query, collect, and upload data. In order to manage the data on the cloud platform, an access control module is set up. The access control module connects to the user accounts connected to the cloud platform to manage and restrict user access to data collection, uploading, modification, and querying operations.

[0046] The cloud platform has a data query module. After connecting to the cloud platform, user accounts with query permissions send a user query request to the data query module. The query module performs data analysis and processing based on the received user query request data to obtain the corresponding query results and feed them back to the user. The cloud platform records each user access and the user's collection, upload, and query operations and saves them in the form of logs, providing basic data for subsequent DTS data traceability and cloud platform data management.

[0047] The cloud platform can not only upload collected DTS data through data interfaces, but also query stored DTS data by sending user query requests. Data retrieval is achieved through a data query module, which includes at least one of the following units: a hierarchical retrieval unit, an image search unit, and an interactive Q&A unit. The hierarchical retrieval unit is used to locate data based on multiple hierarchical positioning methods according to the user's query request. These multiple dimensions include at least two of the following: brand, vehicle series, vehicle model, region, and cross-section. The image search unit searches for similar images stored in the cloud platform's database based on the image uploaded by the user's query request, according to image similarity. The interactive Q&A unit performs text queries based on keywords in the user's query request, analyzes the request intent, and provides corresponding results.

[0048] The data query module also includes a data export unit, which is used to export the queried DTS data according to the user's query request in a preset file format, so that the user can obtain the required DTS data after querying.

[0049] The Intelligent Vehicle DTS Data Acquisition and Query System is an intelligent platform designed for vehicle data acquisition, storage, and querying. It efficiently processes and stores vehicle DTS data, helping users conveniently obtain the information they need in different scenarios. The system supports both automatic and manual data acquisition modes to meet the needs of different users, and its intelligent query function greatly improves the efficiency of data acquisition. Figure 1 shows the 100 functional modules of the Intelligent Vehicle DTS Data Acquisition and Query System, including:

[0050] 1. Access Control Module 101

[0051] Access permissions are set according to user roles to ensure that users with different roles can flexibly and securely access system functions, protecting data security and integrity.

[0052] User roles can be configured as needed. For example, user roles can include at least one of the following: design engineer, quality inspector, administrator, and general visitor. A design engineer can modify part design values ​​and analyze vehicle model comparison data, but has no right to collect actual measurement data; a quality inspector can only enter measurement data and view basic information, and cannot access sensitive quality trend analysis functions; an administrator has full module permissions (including log auditing), while a general visitor can only perform basic queries and cannot export data. This granular permission allocation ensures that each role efficiently completes its assigned tasks while preventing the risk of data leakage or tampering due to unauthorized operations.

[0053] 2. Basic Data Maintenance Module 102

[0054] Manage and maintain basic data, such as cross-section, vehicle, and part information, to ensure data accuracy and consistency, and provide video tutorial support to improve data collection efficiency.

[0055] A cross section is a two-dimensional sectional division in automotive design of the connection points or specific locations of interior and exterior trim components, such as the seam between the door and the body, or the mating surface between the front bumper and the headlights. The system manages the measurement point data (such as design values ​​and measured values ​​of gaps and surface differences) of these cross sections through a cross section information table (including interior / exterior trim classification, area division, and relationships between adjacent parts), and supports rapid location using image or hierarchical search methods, providing a structured data foundation for dimensional accuracy analysis and design optimization.

[0056] For example, the system manages standardized data required for vehicle development through a basic data maintenance module. For instance, the system defines the measurement point name, nominal value (e.g., gap 3.5mm ± 0.2mm), tolerance range, and associated part number at the joint between the headlight and bumper of a certain vehicle model in the cross-section information table, and uploads a video tutorial on the measurement operation of that cross-section. When quality inspectors enter actual measurement data on their mobile devices, the system automatically retrieves the design specifications and demonstration videos for the corresponding cross-section, ensuring the standardization of measurement actions and data formats. This avoids errors caused by human memory and reduces the training cost for new employees.

[0057] 3. Data Acquisition Module 103

[0058] It supports multiple data acquisition methods (manual input, Excel import, and automatic measurement devices) and communicates with devices in real time via network to automatically enter data, improving data acquisition efficiency and accuracy.

[0059] 4. Data Processing Module 104

[0060] It calculates key quality indicators, performs trend analysis and vehicle model comparisons to help users monitor the quality of measurement data and support quality management and design optimization.

[0061] The system automatically calculates key quality indicators and generates analysis reports through the data processing module. For example, for three consecutive batches of measurement data on the gaps in the doors of a certain car model, the system calculates the CPK value (process capability index) to be 1.2 and generates a trend chart showing that the gap size fluctuation gradually exceeds the tolerance range. At the same time, by comparing the data of the same part of the competing models, it is found that their CPK is stable above 1.5. This identifies the mold wear problem of this model, provides the design team with optimization directions, and triggers production equipment maintenance warnings, shortening the quality rectification cycle.

[0062] 5. Data Query Module 105

[0063] It offers multiple query methods, including hierarchical search, image search, and interactive Q&A, to help users quickly obtain data and support data export, thereby improving decision-making efficiency.

[0064] 6. Log Management Module 106

[0065] Automatically record and manage user operation logs, support log querying, anomaly monitoring and cleanup, and improve system transparency, security and traceability.

[0066] The system's log management module can record user actions in real time. For example, if a quality inspector logs into the system at 14:30 on May 15, 2023, using employee ID A203, and modifies the tolerance standard for the B-pillar section of a vehicle model (original value 0.8mm, new value 1.0mm), the system automatically generates a log entry containing the operation time, user ID, and a snapshot of the data before and after the modification. When the quality supervisor searches using the condition "Operation type = data modification + time range = the most recent week," they can quickly locate the change record and initiate traceability based on the declining trend of the DTS pass rate during the same period. The system can also trigger an anomaly alarm if a quality inspector fails to log in five times consecutively within the same day, automatically freezing the account and notifying the administrator via email, achieving end-to-end control of operational risks.

[0067] The intelligent vehicle DTS data acquisition and query system not only effectively improves the efficiency of data acquisition and analysis in the field of intelligent vehicles, but also provides strong technical support for car manufacturers, R&D personnel and technical support personnel, which helps to accelerate the process of product development and quality control and improve the digitalization level of the entire industry chain.

[0068] The equipment required to implement each step of this method includes: manual or automatic measuring equipment, mobile equipment, or a working computer.

[0069] In summary, the method provided in this embodiment can significantly improve the user's work efficiency in automobile design and provide strong data support for vehicle design and production.

[0070] The method provided in this embodiment integrates multiple data entry methods, improving the efficiency and accuracy of data entry, reducing repetitive work, and enhancing work efficiency and measurement quality. Users can choose the appropriate entry method according to different scenarios and needs, greatly improving work flexibility and assisting enterprises in the digital transformation of measurement and management.

[0071] The method provided in this embodiment allows users to search by image and ask questions in an interactive Q&A format without needing professional terminology or experience. Users can simply upload images or ask questions in natural language, and the system will automatically process and return the corresponding accurate information. This makes the entire search process more intuitive and user-friendly, reduces the user's reliance on professional knowledge, and is especially suitable for novice users or scenarios that require quick data acquisition. It also greatly simplifies the search process and improves work efficiency.

[0072] In one optional embodiment, the intelligent vehicle DTS data acquisition and query system 100 is divided into 6 major functional modules, as shown in Figure 1:

[0073] 1. Access Control Module 101

[0074] The access control module allows setting corresponding access permissions based on different user roles, involving multiple data structures such as user information tables, menu information tables, role information tables, department information tables, and job information tables. The system administrator is responsible for granting appropriate roles to users who need to use the system based on their department and job position, and opening the corresponding module menus. The system verifies user permissions and grants authorized module functions to users. This design ensures that users with different roles can flexibly and securely access the functions they need, while effectively protecting the security and integrity of system data. Through this meticulous access control, users can focus on their respective work tasks, improving overall work efficiency.

[0075] 2. Basic Data Maintenance Module 102

[0076] The basic database includes tables for cross-section information, vehicle types, structure types, material types, parts lists, and dictionary information. To ensure the system's proper functioning, this basic information needs to be maintained and updated regularly. Maintaining this information not only helps ensure data accuracy and consistency but also provides users with reliable query and analysis support. This systematic management will improve the overall performance of the system, ensuring users receive accurate and timely information during use, thereby optimizing workflows.

[0077] The cross-section information table manages cross-section-related information. Depending on location, cross-sections can be divided into interior and exterior sections, and further subdivided into multiple areas based on different parts of the vehicle. Each area contains multiple parts, with cross-sections existing between adjacent parts. The system not only provides operations such as adding, deleting, modifying, and querying area, part, and cross-section information, but also offers the ability to upload and play operation videos. Experts can record video tutorials for DTS (Data Transmission System) acquisition and upload them to the system, helping beginners to view and learn the necessary skills at any time. This function not only facilitates data management for users but also helps them better understand and master related operations through video tutorials, thereby improving the efficiency and accuracy of data acquisition.

[0078] The vehicle type table is used to systematically store basic vehicle information, including key fields such as brand, series, model, and production year. Hierarchical associations (e.g., linking brands, series, and models) enable rapid classification and retrieval, and the table is linked to the DTS design value library to ensure accurate matching of measurement data with vehicle configurations (e.g., differentiated storage of gap standards between new energy vehicles and gasoline vehicles).

[0079] The structure type table is used to define the structural attributes of various parts of the vehicle, such as dividing the body into modules like "door assembly" and "front bulkhead assembly," and recording their assembly relationships (e.g., the door hinge structure type is stamped / cast). Standardized structural codes (e.g., FWD-001 represents the welded structure of the front fender) provide a topological basis for DTS measurement point allocation, avoiding conflicts in measurement point definitions.

[0080] The material type table manages the material properties of vehicle components, including parameters such as material number, name, density, and coefficient of thermal expansion. It automatically matches temperature difference compensation formulas for different materials, ensuring the comparability of measurement data under different environments, and also supports material substitution analysis during lightweight design.

[0081] The parts list stores basic data for all vehicle parts, including part number, name, region, and associated section. By linking the BOM (Bill of Materials) to design drawings, when measurements reveal out-of-tolerance parts, the system can trace back to the supplier batch and production tooling number.

[0082] The dictionary information table maintains a standardized terminology database, such as unifying the unit of measurement (mm / mr) for "gap / surface difference," the definition of tolerance symbols, and the enumerated values ​​of measurement status (not measured / qualified / out of tolerance). Multilingual fields (Chinese / English / Spanish) support cross-border team collaboration, avoiding data entry errors caused by ambiguous terminology and improving the consistency of global R&D data.

[0083] 3. Data Acquisition Module 103

[0084] As shown in Figure 2, the vehicle DTS data acquisition module is a module used to collect and manage data related to the vehicle dimensions technical specifications (DTS). The main functions of this module include:

[0085] Measurement Point Setup 201: Users can easily and quickly add, delete, move, and rotate measurement points visually on the system according to measurement requirements, flexibly setting the measurement point positions for each cross-section of the vehicle to ensure measurement accuracy. For example, users can drag measurement point icons to a specified position on the cross-sectional diagram (such as a headlight corner point), and the system automatically generates spatial coordinates and associates them with measurement point numbers. Batch copying / rotation of measurement points is supported, improving setup efficiency.

[0086] Integrated Multiple Data Entry Methods: This module supports data entry methods for various measurement techniques, adapting to user needs in different work scenarios. Specifically, it includes:

[0087] 1. Manual input: Users can find the information display page corresponding to the sample vehicle based on the brand, series, model and sample vehicle level, and manually input the measurement data into the system one by one.

[0088] 2. Excel file import: The system supports importing measurement data from various Excel templates. Users can directly import historical measurement files, facilitating data integration and use.

[0089] 3. Direct import from automatic measuring equipment:

[0090] Before measuring the DTS (Digital Switching) information of a prototype vehicle of a model that has not been measured before, users need to upload cross-sectional images to the system one by one and set the name, location, nominal value, tolerance, and other information of the measurement points. This setting only needs to be done once, and subsequent settings for other prototype vehicles of the same model do not need to be repeated. For self-developed models, the nominal values ​​and tolerances of the measurement points will be directly obtained from the cross-sectional DTS design values ​​of the system, eliminating the need to re-enter the DTS design values ​​of the measurement points, breaking down the barriers between design and measurement, reducing a lot of repetitive work, and speeding up work efficiency. In addition, users need to ensure that the network connection between the system and the automatic measurement equipment is normal for effective communication.

[0091] During measurement, the user simply clicks the "Start Automatic Measurement" button on the vehicle's information page, and the system automatically transmits the pre-set measurement point information to the automatic measurement equipment via the network. Upon receiving the measurement point information, the equipment performs automatic measurements based on this information. Once all measurement points are measured, the equipment automatically generates a fixed-template DTS measurement information table and transmits it back to the system.

[0092] Upon receiving the DTS measurement information form, the system automatically parses the file and stores the parsed information in the measurement point information table of the database. This optimized process not only significantly reduces the workload of manual data entry and improves work efficiency, but also enhances data consistency and measurement accuracy.

[0093] Data storage and management: The collected data will be stored in a structured format to facilitate subsequent querying, analysis, and report generation. This storage method ensures data security and consistency, allowing users to quickly retrieve the information they need when required.

[0094] Through these functions, the vehicle DTS information acquisition module not only improves the efficiency and accuracy of data acquisition, but also provides important technical support for vehicle design and development.

[0095] As shown in Figure 2, the intelligent vehicle DTS data acquisition module adopts a four-level hierarchical structure of "brand-vehicle series-model-section / prototype". The section branch contains four core components: a section list system for systematically classifying and managing the standard sections of the entire vehicle; a section diagram providing 2D / 3D engineering drawings with dimension annotations and supporting zoomable hotspot interaction; basic section information recording metadata such as material properties and process requirements; and DTS design value storage of nominal values ​​and tolerance zones for each measuring point. The parallel prototype branch achieves visualized point placement through measuring point settings and integrates three data acquisition methods: manual entry, Excel import, and automatic measurement. This ensures intelligent correlation between measured data and design values, forming a digital DTS closed-loop management system covering all sections of the entire vehicle.

[0096] 4. Data Processing Module 104

[0097] The main functions of the data processing module include:

[0098] Calculation of DTS-related indicators: This module can calculate key quality indicators including CP, CPK, 6σ, symmetry, parallelism, etc., to evaluate and monitor the quality and consistency of measurement data.

[0099] DTS Trend Analysis for the Same Model: This module supports trend analysis of DTS data for the same model, helping users identify potential problems in the design or manufacturing process and track performance trends.

[0100] DTS Comparison of Different Models: Users can compare the DTS of different models to intuitively understand the differences in size and technical specifications of each model, supporting more effective decision-making and design optimization.

[0101] Calculation of relevant indicators for self-developed models: This module can calculate the out-of-tolerance rate and pass rate of self-developed models, further improving quality monitoring capabilities.

[0102] Through these functions, the data processing module provides users with powerful analytical tools to help them gain a deeper understanding of the data and improve the quality management level of automobile design and manufacturing.

[0103] 5. Data Query Module 105

[0104] The data query module allows for comprehensive access to all relevant information, covering various basic data (such as matching, vehicle series, model, cross-section, DTS design values, etc.), as well as sample vehicle measurement point information, statistical information, trend analysis, and comparative study results. These functions help users gain a deeper understanding of the data and its changes, providing strong support for making more informed decisions.

[0105] The data query module mainly includes the following functions:

[0106] Hierarchical Search: Users can perform hierarchical retrieval based on multiple dimensions such as brand, vehicle series, model, region, and cross-section to quickly find the information they need. Optionally, users can select hierarchical structures such as "brand, vehicle series, model, body region, specific cross-section" step by step. For example, by selecting the joint cross-section of the front bumper and fender of a certain vehicle model, the system will automatically present all related data of that cross-section, including design standard values, tolerance ranges, historical measurement records, and 3D structural diagrams, enabling rapid data location and analysis from the whole vehicle to local measurement points.

[0107] Image search: Users can upload cross-sectional images, and the system uses an improved perceptual hash algorithm to quickly find relevant data for similar cross-sections, significantly improving query efficiency. For example, if a user uploads a photo of a cross-section of the junction between a car headlight and hood, the system uses the perceptual hash algorithm to extract image features and matches the top 5 most similar cross-sections in the database. If it matches the headlight and hood junction designs of 3 different car models, it returns the corresponding DTS standard value, tolerance data, and 3D assembly diagram for each cross-section. Users can intuitively compare the design differences between different car models without manually entering any text information.

[0108] Interactive Question Answering: Based on technologies such as natural language processing and knowledge bases, users can perform text queries by entering keywords. The system will automatically analyze the user's request and provide relevant information. This module is a domain-specific interactive question-and-answer system designed to enhance user experience and make information retrieval more intuitive and convenient. Through natural language interaction with the system, users can quickly find the data they need, enhancing the system's usability and efficiency.

[0109] Data export function: The system supports exporting query results to Excel or other formats, which facilitates users to conduct subsequent analysis and report generation, and improves the efficiency of data utilization.

[0110] Through these functions, the data query module significantly improves the efficiency of users in obtaining information, simplifies the data retrieval process, and assists in decision support for automobile design and manufacturing.

[0111] 6. Log Management Module 106

[0112] The main functions of the log management module include:

[0113] Log Recording: The system automatically records detailed information for all user operations, including login, data query, data entry, modification, and deletion. Each log entry includes the operation time, user ID, operation type, and specific content, facilitating subsequent auditing and tracking. This feature helps analyze the causes and background of operational errors, thereby enabling more effective problem localization and repair.

[0114] Log Query: Users can quickly query specific log records based on criteria such as time range, user ID, and operation type. This feature helps administrators and users understand system usage in a timely manner and ensures a clear grasp of the operation history.

[0115] Anomaly monitoring: The module has anomaly monitoring function, which can automatically identify and record abnormal behaviors, such as frequent login failures or unauthorized data access, and send alarms to the administrator in a timely manner to ensure system security.

[0116] Log cleanup: This module supports periodic cleanup of expired logs to maintain efficient system operation and data cleanliness. At the same time, this feature ensures compliance with data storage and privacy protection regulations, preventing unnecessary data accumulation.

[0117] Through these features, the log management module not only improves system transparency and traceability but also enhances security, providing users and administrators with comprehensive operation logs and data analysis support. This allows users to better understand system operation and ensures system stability and security.

[0118] The data query module offers three query methods. Hierarchical retrieval is a traditional information retrieval method that requires users to make multiple selections to locate the specified information, a process that is relatively cumbersome and inefficient. To address this, the query module also provides two more intelligent and convenient query methods: image search and interactive Q&A.

[0119] Figure 3 illustrates an image-based search query method, also known as a similar cross-section search. This allows users to upload a cross-section image (301) and search for all similar images in the cloud-based cross-section image set. The system sorts these images based on similarity and returns the Top M results with the highest similarity for the user to view. This query method significantly improves the efficiency of information retrieval, enabling users to quickly find relevant cross-sectional data, thereby more effectively supporting design and decision-making.

[0120] As shown in Figure 3, the processing mainly targets the cross-sectional image set stored in the cloud and the query cross-sectional images uploaded by users.

[0121] For each image in the cross-sectional image set, the process is as follows:

[0122] 1. Image preprocessing 302:

[0123] Color space conversion: Convert RGB images to grayscale images to reduce data complexity.

[0124] Image scaling: Scaling images to a preset size, such as 800x800 pixels, to ensure consistency in subsequent processing.

[0125] Mean filtering: Applying mean filtering removes noise from an image, making the image smoother and improving the effect of subsequent feature extraction.

[0126] 2. Feature Extraction 303:

[0127] Edge detection: The Canny edge detection algorithm is used to extract edge information in the image to help identify shape contours.

[0128] Feature descriptor calculation: The ORB algorithm is used to extract key points and their descriptors from the image. These features effectively represent the uniqueness of the image.

[0129] Feature vector construction: The extracted feature descriptions are integrated into feature vectors to form the feature representation of each image.

[0130] 3. Image secondary processing 304:

[0131] Image resizing: The image is scaled down to 8x8 pixels for efficient matching.

[0132] Secondary mean filtering: The mean filter is applied again to further remove noise.

[0133] Binarization: Converting a grayscale image into a black and white image to highlight specific features or objects for easier subsequent analysis.

[0134] 4. Hash value generation 305:

[0135] The image after secondary processing is flattened into a one-dimensional array, and a hash value of fixed length is generated as the unique identifier of the image.

[0136] 5. Feature index database update 306:

[0137] Information such as image ID, feature vector, and hash value is written into the image feature index library for fast querying.

[0138] The process for querying the cross-sectional image is as follows:

[0139] 1. Image Upload 312:

[0140] Users can upload or take a clear cross-sectional image to facilitate similarity analysis by the system. This step supports mobile devices to ensure a smooth user experience.

[0141] 2. Image Preprocessing 307:

[0142] The uploaded images are processed sequentially, including color space conversion, scaling to 800x800 pixels, and mean filtering, to reduce computational complexity and highlight shape features, thereby improving matching accuracy.

[0143] 3. Image secondary processing 308:

[0144] The preprocessed image is then scaled twice to 8x8 pixels, subjected to two mean filters, and binarized.

[0145] 4. Hash value generation 309:

[0146] Generate the hash value of the query image.

[0147] 5. Coarseness similarity calculation 310:

[0148] Calculate the Hamming distance between the hash value of the query image and the hash values ​​of all images in the feature index to obtain a coarse similarity score for each pair of images.

[0149] 6. Result Sorting and Extraction 311:

[0150] Results sorting: The matching results are sorted according to the coarse similarity score, and the image with the highest similarity is selected.

[0151] Top N Results Extraction: Extract the top N matching results with the highest similarity for further analysis.

[0152] 7. Feature Extraction

[0153] Edge detection, feature descriptor calculation, and feature vector construction are performed on the preprocessed image.

[0154] 8. Fineness Similarity Calculation 313:

[0155] Calculate the Euclidean distance between the feature vector of the query image and the feature vectors of the top N matching results to obtain the detail similarity score for each pair of images.

[0156] 9. Secondary sorting and extraction 314:

[0157] The fine-grained similarity calculation results are sorted, and the top M matching information is extracted.

[0158] 10. Results Display 315:

[0159] The system integrates relevant vehicle information for each matching result, including brand, model, production year, DTS design values, and measurement values, and displays it on the user interface. This not only allows users to intuitively understand the appearance of similar cross-sections but also provides necessary background information, such as materials and manufacturing processes, to assist them in decision-making and design reference.

[0160] Through this series of processes, users can quickly and accurately find vehicle cross-sections similar to the target cross-section, significantly improving design efficiency and innovation capabilities, and helping companies maintain their advantage in fierce market competition.

[0161] The interactive question-and-answer query method, as shown in Figure 4, is an important function of the intelligent vehicle DTS data acquisition and query system. It aims to provide users with a convenient way to obtain information through natural language processing technology. Specifically, this function has the following key features:

[0162] Natural Language Understanding: Users can ask questions in natural language without using complex technical terms or fixed query formats. For example, a user can ask, "What is the latest DTS pass rate for a certain vehicle model?" or "How do I measure the measurement points from the headlight to the front bumper section?" The system will automatically parse the user's question and extract key information.

[0163] Knowledge Base Support: The system has a built-in rich knowledge base covering DTS standards, measurement methods, common problems, and solutions for various vehicle models. The interactive Q&A function can extract relevant information from the knowledge base in real time to ensure users receive accurate and timely answers.

[0164] Diverse question types: Users can ask various types of questions, including querying DTS measurement data for a specific vehicle model; understanding the usage methods and applicable scenarios of different measurement tools; obtaining historical trends or comparative analysis information of DTS data; and seeking technical support or solutions, such as "How to handle measurement errors?"

[0165] Database enhancement: The system regularly updates its knowledge base to enhance its intelligence and usability.

[0166] User-friendly interface: this function is presented through an intuitive user interface. A user only needs to input a question in the input box, and the system will return an answer in a clear format, improving user experience.

[0167] Multilingual support: to meet the needs of different users, the system can support multiple languages, ensuring that a wider user group can use it smoothly.

[0168] Interactive suggestions: after the user puts forward a question, the system not only provides a direct answer, but also recommends relevant questions or topics to help the user gain an in-depth understanding of relevant information. For example, when the user inquires about the DTS data of a certain vehicle model, the system can recommend subsequent topics such as "measurement method of this vehicle model" or "comparative analysis of similar vehicle models".

[0169] Through the above features, the interactive question-and-answer query method can effectively improve the user's work efficiency and reduce the dependence on professional knowledge. Meanwhile, it provides users with richer information support and promotes the rapid collection and analysis of intelligent vehicle DTS data.

[0170] The specific process of the interactive question-and-answer query method is described as follows:

[0171] Inputting query statement 401: the user first inputs the content they want to know in the query box of the system, which is usually a question or instruction put forward in the form of natural language.

[0172] Text preprocessing 402: the system will perform preliminary text preprocessing on the user's query statement, including processing steps such as removing unnecessary punctuation marks, stop words (such as "de", "le" in Chinese, etc.) and lemmatization (converting words in different forms to their basic forms, such as "measure" and "measured"), so as to ensure that the system can understand the user's question more accurately.

[0173] Word vector conversion 403: the preprocessed text is converted into word vectors, which are a numerical representation form that encodes text information for machines to understand and process.

[0174] Knowledge retrieval 404: the converted word vectors will be matched and retrieved with data in the vehicle DTS knowledge base. The knowledge base contains a large amount of DTS data and related information, and the system finds the content most relevant to the user's query by searching the knowledge base.

[0175] Answer generation 405: according to the retrieved relevant information, the system uses a large language model in the DTS field to generate an appropriate answer. The language model comprehensively considers the information in the knowledge base as well as domain-specific terms and rules.

[0176] Answer Post-Processing 406: After the answer is generated, the system will further process the generated answer to ensure the accuracy and ease of understanding of the answer, including word order optimization, format adjustment, etc.

[0177] Answer Display 407: The processed answer is presented to the user, who can then perform further actions or submit new queries based on the returned results.

[0178] The implementation of this embodiment results in the following specific characteristics for the collection and querying of DTS data:

[0179] 1. Offers multiple query methods to accelerate user information retrieval efficiency: The system features diverse query functions, enabling users to quickly find the information they need through various methods. Users can upload cross-sectional images for image-based search to quickly obtain relevant information about similar cross-sections; through interactive Q&A, users can interact with the system using natural language to obtain accurate domain information; simultaneously, the hierarchical query function allows users to precisely search by multi-level categories such as brand, car series, and model. This flexible query approach significantly improves the speed and accuracy of information retrieval, substantially enhancing user work efficiency.

[0180] 2. Offers multi-platform applications to meet user needs in different usage scenarios: The system supports both web and mobile platforms, each independently designed and implemented for its respective application scenario. This multi-platform design allows users to conveniently access and use the system's functions and efficiently input data, whether in the office or at the measurement site, using either automatic or manual data acquisition devices. This not only enhances the user experience but also enables efficient data acquisition and analysis in various environments, fully meeting users' flexible usage needs.

[0181] 3. Module-level access control to meet the needs of different user roles: The system implements detailed module-level access control, allowing for the setting of corresponding access permissions based on different user roles. This means that users with different roles, such as designers, data acquisition personnel, and management, can access specific modules on the operating system according to their respective needs and responsibilities. This flexible access control mechanism ensures data security while also improving work efficiency, enabling each user to focus on tasks within their scope of responsibility and effectively reducing errors or chaos caused by improper permissions.

[0182] Obviously, the specific implementation of this application is not limited to the above-mentioned methods. Any non-substantial improvements made using the inventive concept and technical solution of this application are within the scope of protection of this application.

Claims

1. An intelligent vehicle DTS data acquisition and query system, wherein: The system includes a cloud platform, which is configured with a data acquisition port; The cloud platform is used to acquire DTS data through the data acquisition port; The cloud platform is used to identify and process the collected DTS data, and store the processed DTS data in a database, which is a structured database.

2. The intelligent vehicle DTS data acquisition and query system as described in claim 1, wherein: The cloud platform includes a data acquisition module; The data acquisition module is used to connect to the data acquisition device through the data acquisition interface. The data acquisition device supports manual and automatic acquisition of DTS data and sends the DTS data to the data acquisition module through the data acquisition interface. The data acquisition module parses and processes the uploaded DTS data to obtain storable DTS data and stores it in the database.

3. The intelligent vehicle DTS data acquisition and query system as described in claim 2, wherein: The data acquisition device is connected to the data acquisition interface via a network. The data acquisition interface is a network service interface. The data acquisition device is used to upload the DTS data to the cloud platform and send it to the database through the data acquisition interface. The data acquisition device includes at least one of the following: a web terminal device, a mobile terminal device, and an automatic testing device. The data acquisition device is used to realize data input and acquisition.

4. The intelligent vehicle DTS data acquisition and query system as described in claim 3, wherein: The web-based device and / or the mobile device inputs the DTS data through a pre-developed templated webpage; or, the web-based device and / or the mobile device inputs the DTS data by importing an Excel file, and the data acquisition module is used to parse the DTS data in the Excel file.

5. A smart vehicle DTS data acquisition and query system as described in any one of claims 1 to 4, wherein: The cloud platform also includes a data processing module, which is used to calculate evaluation indicators based on the collected DTS data. These evaluation indicators are used to evaluate the design and manufacturing level of the vehicle.

6. The intelligent vehicle DTS data acquisition and query system as described in claim 5, wherein: The evaluation indicators include at least one of the following: DTS-related instruction indicators, DTS trend indicators for the same vehicle model, and DTS comparison indicators for different vehicle models.

7. The intelligent vehicle DTS data acquisition and query system as described in claim 1, wherein: The cloud platform also includes a permission management module, which is used to manage user accounts connected to the cloud platform and to restrict and manage user data collection, uploading, modification, and query operations.

8. A smart vehicle DTS data acquisition and query system as described in any one of claims 1 to 7, wherein: The cloud platform is equipped with a data query module; The data query module is used to perform data analysis and processing on the received query request data when a user account with query permissions sends a query request to the data query module after accessing the cloud platform, obtain the corresponding query results, and feed them back to the user account.

9. The intelligent vehicle DTS data acquisition and query system as described in claim 8, wherein: The data query module includes at least one of the following units: a hierarchical retrieval unit, an image search unit, and an interactive question and answer unit. The hierarchical retrieval unit is used to perform data query and location based on a hierarchical positioning method with multiple dimensions according to the user's query request. The multiple dimensions include at least two of the following dimensions: brand, vehicle series, vehicle model, region, and cross-section. The image search unit is used to query the DTS data of similar images stored in the database of the cloud platform according to the image uploaded by the user's query request and the image similarity. The interactive question-and-answer unit is used to perform text queries based on keywords in the user's query request, analyze the request intent, and provide corresponding results.

10. The intelligent vehicle DTS data acquisition and query system as described in claim 9, wherein: The data query module also includes a data export unit, which is used to export the queried DTS data according to a preset file format based on the user's query request.

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