Automobile industry data visualization data comprehensive processing method based on digital twinning

Abstract

The application relates to the technical field of data processing, and discloses a kind of automobile industry data visualization data comprehensive processing method based on digital twinning.The method comprises the following steps: the three-dimensional space of automobile assembly workshop is recursively quadricut by workshop process pixel quadtree index algorithm, the process type of multi-source heterogeneous data is marked according to process identification mapping rule, the real-time data set of workshop is distributed to quadtree index structure according to spatial coordinates to carry out process state aggregation, the pixel comprehensive display value is obtained by weighting aggregation calculation of process state aggregation matrix according to viewport pixel mapping relationship, and the multi-process parallel scene of workshop is handled by hierarchical rendering based on pixel comprehensive display value.The application improves the visualization processing efficiency and user interaction response speed of automobile industry digital twinning system.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method for comprehensive processing of automotive industry data visualization data based on digital twins. Background Art

[0002] Digital twin technology in the automotive manufacturing industry has been widely used for production process monitoring and process optimization. Traditional digital twin visualization methods are primarily based on data-driven rendering architectures, directly processing real-time status data of various equipment within the workshop, production process parameters, and logistics scheduling information to generate 3D visualization scenes. Existing digital twin systems collect real-time data from multiple heterogeneous systems, such as ERP, MES, WMS, and IoT, through industrial protocols such as OPC UA and Modbus. They then utilize traditional geometric rendering pipelines to present information such as workshop equipment status, logistics trajectories, and abnormality alerts to users in the form of 3D graphics. Simultaneously, they integrate simulation engines to enable real-time mapping and predictive analysis of production line operation status.

[0003] However, existing digital twin visualization methods face significant performance bottlenecks when processing large-scale dynamic workshop data. Their computational complexity increases exponentially with the number of devices, sensor nodes, and real-time status information within the workshop. When an automotive assembly workshop needs to simultaneously visualize tens of thousands of dynamic objects, traditional data-driven rendering methods struggle to maintain smooth real-time responses under complex perspective transformations and zoom operations, resulting in a poor user interaction experience and inefficient decision-making. Existing rendering algorithms rely on traversing all workshop data to perform state calculations and geometric transformations. They lack effective spatial indexing and data aggregation mechanisms, making it impossible to fully utilize the limited pixel resources of the viewport display area to optimize computational overhead.

[0004] Based on the above analysis, the problem with existing technologies is that they fail to effectively decouple visual display requirements from data processing complexity, resulting in system performance being limited by the scale of workshop data rather than the user's actual visual perception needs. Further analysis revealed that traditional methods lack a spatial data organization structure tailored to the characteristics of automotive manufacturing workshop processes, making it impossible to achieve pixel-level data aggregation and state comprehensive calculations. At the same time, they lack a layered rendering architecture to optimize the visual presentation of different types of information in multi-process parallel scenarios. Therefore, there is an urgent need for a digital twin visualization data comprehensive processing method that can transform the computational complexity from being dependent on the scale of workshop data to being dependent on the number of viewport pixels. By constructing a process pixel quadtree index structure and a weighted calculation algorithm based on pixel aggregation, real-time visualization processing of large-scale workshop dynamic data can be achieved. Summary of the Invention

[0005] This application provides a comprehensive data processing method for automotive industry visualization based on digital twins. This method addresses the real-time rendering performance bottleneck caused by the exponential growth of computational complexity with the scale of workshop data in traditional digital twin visualization methods, as well as the lack of spatial data organization and pixel-level aggregation processing mechanisms tailored to the specific characteristics of automotive manufacturing processes. This application improves the visualization processing efficiency and user interaction response speed of automotive industry digital twin systems.

[0006] The present application provides a method for comprehensive processing of automotive industry data visualization data based on digital twins, the method comprising:

[0007] The three-dimensional space of the automobile assembly workshop is recursively divided into four parts by using the workshop process pixel quadtree index algorithm to obtain the workshop process pixel quadtree index structure.

[0008] Perform process type tagging on multi-source heterogeneous data according to process identification mapping rules to obtain a real-time workshop data set containing process type identification;

[0009] Allocating the workshop real-time data set to the workshop process pixel quadtree index structure according to spatial coordinates to perform process state aggregation processing to obtain a process state aggregation matrix;

[0010] Performing weighted aggregation calculation processing on the process state aggregation matrix according to the viewport pixel mapping relationship to obtain a pixel comprehensive display value;

[0011] Based on the pixel comprehensive display value, a multi-process parallel scene in a workshop is subjected to layered rendering processing to obtain a three-dimensional visualization result of an automobile industry database.

[0012] The technical solution provided in this application recursively quad-segments the three-dimensional space of an automobile assembly workshop using a workshop pixel quadtree indexing algorithm, establishing a spatial data organization structure tailored to the characteristics of automobile manufacturing processes. Compared to traditional linear data storage methods, the quadtree indexing structure implements hierarchical partitioning based on the spatial distribution characteristics of processes such as frame processing, powertrain assembly, and PBS scheduling. This reduces the time complexity of data query and spatial positioning from O(n) to O(log n), significantly improving the retrieval efficiency of large-scale workshop data. Process identification mapping rules label multi-source heterogeneous data by process type, resolving the issues of inconsistent data formats and semantics from different systems such as ERP, MES, and WMS in traditional methods. A unified process type coding system enables semantic alignment and structured processing of heterogeneous data, laying a solid foundation for subsequent data aggregation calculations. The real-time workshop dataset is assigned to a quadtree indexing structure based on spatial coordinates for process state aggregation, changing the traditional method of processing equipment data one by one. Aggregating states within spatial regions reduces repeated calculations. Simultaneously, the establishment of a process state aggregation matrix enables efficient organization and rapid access to complex multidimensional state information. The core innovation of weighted aggregation calculations of process status aggregation matrices based on viewport pixel mapping relationships lies in converting the traditional computational complexity that depends on the scale of workshop data to one that depends on the number of viewport pixels. When the number of viewport pixels is much smaller than the amount of workshop process data, this conversion brings an order of magnitude performance improvement, enabling the system to achieve near-real-time interactive response while ensuring visual quality. Layered rendering is performed on parallel scenes of multiple workshop processes based on the integrated pixel display values. A four-layer rendering architecture is constructed, consisting of a basic environment layer, an equipment status layer, a logistics trajectory layer, and an anomaly alarm layer. Each rendering layer independently processes specific types of information, avoiding the visual conflicts and performance losses caused by the mixed rendering of different types of information in traditional methods. The layered architecture also supports parallel processing of different rendering layers, fully leveraging the parallel computing capabilities of modern GPUs.

[0013] In the specific application domain of digital twins in the automotive industry, the workshop process pixel quadtree indexing algorithm fully considers the spatial layout characteristics of processes within the final assembly workshop, such as the frame processing line, powertrain assembly line, PBS area, and interior chassis assembly line. The algorithm's recursive segmentation strategy adaptively adjusts based on process density and equipment distribution, ensuring that the index structure better matches the actual production layout, thereby enhancing adaptability and scalability as the workshop layout changes. The process type labeling algorithm is specifically designed to address the process flow characteristics of automotive manufacturing. Using a predefined process type coding system, the algorithm automatically identifies and classifies data characteristics of different processes. This domain-specific data processing approach offers higher accuracy and efficiency than general data integration methods. The equipment and material status weighting coefficients used in the weighted aggregation calculation process fully reflect the production characteristics of the automotive manufacturing industry. Equipment in an operational state contributes most to the production cycle, equipment in a faulty state poses the most significant risk, and sufficient materials provide the strongest guarantee for production continuity. The rational setting of these weighting coefficients ensures that the pixel composite display value accurately reflects the actual production status of the workshop, providing reliable decision-making support for production managers. The design of the layered rendering architecture takes into account the hierarchy and importance of information types in automotive manufacturing workshops. The basic environment layer provides spatial positioning references, the equipment status layer reflects production activity, the logistics trajectory layer displays material flow status, and the abnormal alarm layer highlights key issues. This layered design allows users to flexibly adjust the display priority of different information layers according to management needs, improving the practicality and user experience of the digital twin system in automotive industry applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 This is a schematic diagram of an embodiment of a method for comprehensive processing of automotive industry data visualization data based on digital twins in an embodiment of the present application. DETAILED DESCRIPTION

[0016] An embodiment of the present application provides a method for comprehensive processing of automotive industry data visualization data based on digital twins. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0017] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of a method for comprehensive processing of automotive industry data visualization based on digital twins includes:

[0018] Step S101: recursively quad-segment the three-dimensional space of the automobile assembly workshop using a workshop process pixel quad-tree index algorithm to obtain a workshop process pixel quad-tree index structure;

[0019] Step S102: Process type labeling is performed on the multi-source heterogeneous data according to the process identification mapping rule to obtain a workshop real-time data set containing the process type identification;

[0020] Step S103: Allocate the workshop real-time data set according to the spatial coordinates to the workshop process pixel quadtree index structure to perform process state aggregation processing to obtain a process state aggregation matrix;

[0021] Step S104: performing weighted aggregation calculation processing on the process state aggregation matrix according to the viewport pixel mapping relationship to obtain a pixel comprehensive display value;

[0022] Step S105 : performing layered rendering processing on the multi-process parallel scene of the workshop based on the pixel comprehensive display value to obtain a three-dimensional visualization result of the automobile industry database.

[0023] It is understandable that the execution subject of this application can be a digital twin-based automotive industry data visualization data comprehensive processing system, or a terminal or server, which is not specifically limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0024] Specifically, the three-dimensional space of the automobile assembly shop is divided into cubical regions according to the process distribution characteristics to obtain initial spatial segmentation units. Then, the process elements of these initial spatial segmentation units are spatially positioned based on the coordinates of the frame processing line stations, the path nodes of the powertrain assembly AGV, and the storage location of the vehicle head in the PBS area to obtain process spatial positioning data. Based on the process spatial positioning data, a recursive quad-segmentation calculation is performed on the initial spatial segmentation units. Each quadtree node corresponds to a cubical spatial region, and the node stores the aggregate information of all process elements in the region, including the process status aggregation matrix, equipment quantity statistical vector, and material quantity statistical vector. Each node is weighted according to the process weight coefficient, resulting in a weighted quadtree index structure for the workshop process pixels.

[0025] Data from the frame processing line PLC, powertrain assembly AGV system, PBS regional scheduling system, interior mainline MES, and chassis assembly line sensors are collected via the OPC UA and Modbus protocols to obtain raw equipment data. Process type codes are assigned to the raw equipment data based on the data source system: frame processing line PLC data is labeled as process type 1, powertrain assembly AGV system data is labeled as process type 2, PBS regional scheduling system data is labeled as process type 3, interior mainline MES data is labeled as process type 4, and chassis assembly line sensor data is labeled as process type 5. This yields process-identified equipment data. Data records with process identifiers are structured based on timestamps, equipment numbers, and spatial coordinates. Each data record contains a timestamp T, process type identifier P, equipment number D, spatial coordinates, status value V, and data quality identifier Q, resulting in standardized data records. The data quality identifier is added to the standardized data records to mark the data quality, resulting in complete data records. Metadata association is performed on the complete data records using the data lineage table to obtain a real-time workshop dataset containing process type identifiers.

[0026] Based on the spatial coordinate information in the real-time workshop data set, the weighted workshop process pixel quadtree index structure is node-located to find the corresponding data allocation node. Based on the process type number, equipment status number, and material status number, the data within the data allocation node is classified into three dimensions to obtain process status classification data. The process status classification data is used to perform equipment status statistics according to the operating state, standby state, and fault state. At the same time, material status statistics are performed according to the sufficient state, tense state, and shortage state to obtain equipment status statistics and material status statistics, respectively. Based on the equipment status statistics and material status statistics, a state aggregation matrix is ​​generated for each node. The process status aggregation matrix records the real-time status distribution of each type of process in the spatial area, and contains a three-dimensional matrix structure of process type, equipment status, and material status.

[0027] The display pixels in the digital twin's 3D viewport are spatially mapped using the viewport transformation matrix. Each pixel is mapped to a spatial region in the workshop's 3D spatial coordinate system using the viewport transformation matrix, yielding the pixel's corresponding spatial region. Based on the pixel's corresponding spatial region, the process status aggregation matrix is ​​quickly queried to obtain the process status data within the pixel region. The process status data within the pixel region is weighted based on the equipment status weight coefficient and the material status weight coefficient. The equipment status weight coefficient is 1.0 for the operating state, 0.5 for the standby state, and 0.1 for the fault state. The material status weight coefficient is 1.0 for the sufficient state, 0.7 for the tight state, and 0.3 for the short state. The weighted summation yields the process weighted aggregation value. The equipment density factor and material density factor are input into the density correction algorithm for density correction. The equipment density factor is equal to the total number of equipment divided by the spatial region area, and the material density factor is equal to the total number of materials divided by the spatial region area. The density correction factor is the average of the equipment density factor and the material density factor. The pixel display value is then combined with the process weighted aggregation value and the density correction coefficient to obtain the pixel's composite display value.

[0028] The rendering detail level is graded based on the pixel composite display value range. High detail rendering is used when the pixel composite display value is greater than 0.8, medium detail rendering is used when the pixel composite display value is between 0.5 and 0.8, and low detail rendering is used when the pixel composite display value is less than or equal to 0.5. A hierarchical architecture is constructed for workshop visualization content, based on the basic environment layer, equipment status layer, logistics trajectory layer, and abnormality alarm layer, resulting in a four-layer rendering architecture. Static elements are assigned to the basic environment layer, including a basic environment rendering layer for the workshop building structure, column racks, and workstation layout. Heat map mapping is performed on the equipment status layer based on the pixel composite display value, including a rendering layer for the operating status of the frame processing equipment, power assembly AGVs, PBS scheduling system, and interior chassis assembly lines. Dynamic elements are assigned to the logistics trajectory layer based on material flow paths, AGV operation trajectories, and EMS lifting routes, including a logistics trajectory rendering layer containing dynamic logistics information. Abnormal status information such as equipment failures, material shortages, and process bottlenecks is assigned to the abnormality alarm layer for highlighting, including an abnormality alarm rendering layer with abnormal status indicators. Based on the hierarchical combination of four rendering layers, the three-dimensional visualization result of the automotive industry database is obtained.

[0029] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0030] The three-dimensional space of the automobile assembly workshop is divided into cubic space regions according to the process distribution characteristics to obtain the initial space segmentation units;

[0031] According to the coordinates of the frame processing line station, the powertrain assembly AGV path nodes, and the PBS area head storage position, the initial space segmentation unit is processed for process element spatial positioning to obtain process spatial positioning data;

[0032] Based on the process space positioning data, the initial space segmentation unit is recursively divided into four parts to obtain a quadtree node hierarchy structure;

[0033] Assign the process state aggregation matrix, equipment quantity statistical vector, and material quantity statistical vector to the quadtree node hierarchy for data storage and processing, and obtain the workshop process pixel quadtree index structure;

[0034] According to the process weight coefficient, each node in the workshop process pixel quadtree index structure is weighted and a workshop process pixel quadtree index structure with weights is obtained.

[0035] Specifically, the overall spatial boundary information of the workshop is obtained, including the three-dimensional coordinate range of the workshop's length, width, and height. The partitioning granularity is then determined based on the process distribution characteristics. These process distribution characteristics refer to the spatial distribution density and coverage of different processes within the workshop, such as the frame processing line, powertrain assembly line, PBS area, interior main line, and chassis assembly line. The cubic spatial region partitioning algorithm divides the workshop's three-dimensional space into a grid based on preset cube side lengths. Each cube unit has the same spatial dimensions, determined by the average floor space of the workshop's equipment and the process density. This results in a regularly arranged initial spatial segmentation unit, each containing attribute information such as the starting and ending coordinates, cube volume, and the process region identifier.

[0036] During the spatial positioning of process elements, the coordinates of the frame processing line workstations are obtained from the workshop layout drawings. Each workstation includes information such as the workstation number, three-dimensional coordinate position, workstation type, and processing technology type. The path nodes of the powertrain assembly AGV are obtained from the path planning data of the AGV scheduling system. Each path node includes information such as the node number, coordinate position, path direction, and docking duration. The storage locations of the headstock in the PBS area are obtained from the storage area division of the PBS scheduling system. Each storage location includes information such as the storage location number, coordinate range, storage capacity, and current occupancy status. The process element spatial positioning algorithm spatially matches the coordinate information of these process elements with the coordinate range of the initial spatial segmentation unit, determines which initial spatial segmentation unit each process element falls within, and records the type and number of process elements contained in each initial spatial segmentation unit to obtain process spatial positioning data. The process spatial positioning data includes detailed information such as the spatial segmentation unit number, the list of included process elements, the process element coordinates, and the process element type.

[0037] The recursive quad-segmentation calculation further subdivides the initial spatial segmentation unit based on the process spatial positioning data. The recursive quad-segmentation algorithm first determines the process element density within each initial spatial segmentation unit. When the number of process elements exceeds the preset threshold, the cubic spatial segmentation unit is divided into eight equal parts along the three coordinate axes to obtain eight child cubic units. The same judgment and segmentation process is then recursively performed on each child cubic unit until the number of process elements in each child unit is lower than the threshold or the maximum segmentation depth is reached. In the quadtree node hierarchy, each node corresponds to a cubic spatial area. The parent node contains the spatial range of the child node. The child nodes do not overlap and completely cover the spatial range of the parent node. Each node stores data structures such as the boundary coordinates of the spatial area, the process element information contained, the child node pointer, the parent node pointer, etc., to obtain a complete quadtree node hierarchy.

[0038] The data storage process assigns the process state aggregation matrix, equipment quantity statistics vector, and material quantity statistics vector to the corresponding nodes of the quadtree node hierarchy. The process state aggregation matrix records the state distribution of various processes within the node's spatial area. The matrix rows represent the process type, the matrix columns represent the state type, and the matrix element values ​​represent the quantity statistics of the corresponding process and state. The equipment quantity statistics vector records the quantity distribution of various types of equipment within the node's spatial area, and the vector elements correspond to the quantity values ​​of different equipment types. The material quantity statistics vector records the inventory distribution of various types of materials within the node's spatial area, and the vector elements correspond to the quantity values ​​of different material types. The data storage algorithm determines the quadtree node to which the process element belongs based on its spatial coordinate information, and then accumulates the process element's state information into the aggregation matrix and statistics vector of the corresponding node. At the same time, it maintains data consistency between nodes. When the data of a child node changes, the aggregate data of the parent node is updated accordingly, resulting in a workshop process pixel quadtree index structure.

[0039] The weight assignment process weights each node in the workshop process pixel quadtree index structure based on the process weight coefficient. The process weight coefficient reflects the degree of impact of different processes on production cycle time and product quality. The weight coefficient of frame processing is set to a high value because the frame is the basic structure of the entire vehicle. The weight coefficient of PBS scheduling is also set to a high value because the PBS area is a key control point for production bottlenecks. The weight coefficients of interior assembly and chassis assembly are relatively low. The weight assignment algorithm calculates the comprehensive weight value of each node based on the type and number of processes contained in the node. The calculation method is to multiply the number of each process type by the corresponding process weight coefficient, and then divide the sum by the total number of process elements in the node to obtain the normalized weight value of the node. The result is a weighted workshop process pixel quadtree index structure. In addition to containing the original data information, each node also contains a weight value attribute. The weight value is used for subsequent pixel aggregation calculation and priority judgment in visualization rendering.

[0040] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0041] Data is collected and processed from the frame processing line PLC, powertrain assembly AGV system, PBS regional scheduling system, interior main line MES, and chassis assembly line sensors through the OPC UA and Modbus protocols to obtain raw equipment data.

[0042] According to the data source system, the original equipment data is assigned a process type code to obtain equipment data with process identification;

[0043] Perform data record structure processing on equipment data with process identification based on timestamp, equipment number, and spatial coordinates to obtain standardized data records;

[0044] Add the data quality identifier to the standardized data record to perform data quality marking processing to obtain a complete data record;

[0045] The complete data records are processed with metadata association according to the data lineage table to obtain a real-time workshop data set containing process type identification.

[0046] Specifically, in data acquisition and processing using the OPC UA and Modbus protocols, OPC UA is a standard industrial communication protocol that supports cross-platform device data exchange and features secure authentication and data encryption. Modbus is a serial communication protocol widely used for data transmission in industrial automation equipment. The frame processing line PLC regularly reports workstation status data via OPC UA, including parameters such as workstation operating status, processing progress, equipment temperature, and current value. The data acquisition frequency is set at 10 times per second, and each acquisition includes fields such as the workstation number, status code, numerical parameters, and acquisition timestamp. The powertrain assembly AGV system reports information such as AGV position coordinates, operating speed, battery charge, and task status via the Modbus protocol. The AGV controller sends a data packet every 200 milliseconds. The packet format includes fields such as the AGV number, coordinate X value, coordinate Y value, direction angle, and operating status. The PBS regional dispatch system reports data such as vehicle head storage location information, dispatch instruction execution status, and buffer area occupancy via the OPC UA protocol. The dispatch system triggers data reporting based on vehicle head entry and exit events. The data format includes fields such as storage location number, vehicle head identification code, entry time, exit time, and dispatch status. The interior mainline MES reports production order information, process execution status, quality inspection results, and material consumption via the OPC UA protocol. The MES system reports data synchronously according to the production rhythm. The data format includes fields such as order number, process number, operator number, start time, end time, and quality grade. Sensors on the chassis assembly line report process parameters such as torque value, assembly depth, and bolt preload via the Modbus protocol. The sensors monitor the assembly process in real time, with data acquisition accuracy reaching milliseconds. The data format includes fields such as sensor number, measurement value, measurement unit, and abnormality flag. All collected device data is aggregated to generate raw device data.

[0047] The process type code assignment process classifies and labels the original equipment data based on the data source system. The process type code is digitally coded. Data from the frame processing line PLC is marked with process type code 1, data from the powertrain assembly AGV system is marked with process type code 2, data from the PBS regional scheduling system is marked with process type code 3, data from the interior main line MES is marked with process type code 4, and data from the chassis assembly line sensors is marked with process type code 5. The code assignment algorithm automatically determines the process type to which the data belongs based on the system identifier of the data source. It then adds a process type code field to the original equipment data record. The code value directly corresponds to the corresponding process classification, while retaining the original equipment identification information and data content unchanged, resulting in equipment data with process identification. Equipment data with process identification has a new process type code field added to the original field, and this field value establishes a mapping relationship with the specific process type.

[0048] Data record structuring converts process-identified equipment data into a unified format based on timestamps, equipment numbers, and spatial coordinates. The standardized data record format is defined as a fixed field structure, including fields such as the record's unique identifier, timestamp, process type code, equipment number, spatial coordinate X, Y, and Z values, equipment status, measurement value, and data units. Timestamps are uniformly converted to standard UTC time format with millisecond accuracy. Equipment numbers use a unified encoding scheme, including the process prefix, equipment type identifier, and equipment serial number. Spatial coordinates use the workshop global coordinate system, with the origin set at the workshop entrance, the X axis running along the workshop's length, the Y axis running along the workshop's width, and the Z axis running along the workshop's height. Coordinate units are uniformly expressed in meters. A structuring algorithm traverses the process-identified equipment data, extracting key fields from each record and reorganizing the data structure according to a standardized format. Missing fields are filled with default values, and abnormal field values ​​are cleaned and formatted. This results in standardized data records with a unified data schema and field definitions.

[0049] Data quality tagging adds a data quality identifier to standardized data records. The data quality identifier reflects data quality attributes such as completeness, accuracy, and timeliness. The quality identifier is graded using a grading system, with four levels: excellent, good, fair, and poor, corresponding to quality scores of 4, 3, 2, and 1. The data quality assessment algorithm calculates the quality score based on a combination of data integrity checks, value range checks, timestamp continuity verification, and device state logic consistency checks. The data integrity check verifies that necessary fields are not missing; the value range check determines whether measured values ​​are within a reasonable range; the timestamp continuity verification checks whether the data collection interval is normal; and the device state logic consistency check verifies whether the state changes before and after conform to the device's operating logic. The quality tagging algorithm uses the average of the four check results as the quality identifier for the record and adds a data quality identifier field to the standardized data record. This field records the quality level and specific score, resulting in a complete data record. The complete data record contains the original standardized fields and the newly added data quality identifier field.

[0050] The metadata association processing supplements the complete data record with traceability information based on the data lineage table. The data lineage table records metadata information such as the source path, processing history, and association relationship of each data, including fields such as the data source system name, collection method, transmission path, processing time, and associated data record identifier. The metadata association algorithm searches for the corresponding lineage information in the data lineage table based on the device number and timestamp of the complete data record, establishes an association relationship between the data record and the metadata, and records the data life cycle information, including time nodes such as data generation time, collection time, processing time, and storage time. The association processing result adds a metadata association field to the complete data record. This field contains lineage identifiers, data source information, processing history, and other content, and obtains a real-time workshop data set containing process type identifiers. Each record in the workshop real-time data set has complete data content, quality identifiers, and metadata association information. The data set is indexed and organized according to time series and process type.

[0051] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0052] According to the spatial coordinate information in the real-time data set of the workshop, the weighted workshop process pixel quadtree index structure is processed to obtain the data allocation node;

[0053] Perform three-dimensional state classification processing on the data in the data allocation node based on the process type number, equipment status number, and material status number to obtain process status classification data;

[0054] The process status classification data is processed according to the running state, standby state and fault state to obtain the equipment status statistical results;

[0055] Perform material status statistics on process status classification data according to sufficient status, tight status, and shortage status to obtain material status statistics results;

[0056] Based on the equipment status statistics and material status statistics, a state aggregation matrix is ​​generated for each node to obtain a process state aggregation matrix.

[0057] Specifically, node localization accurately matches the weighted quadtree index structure of workshop process pixels based on the spatial coordinate information in the workshop's real-time dataset. This spatial coordinate information includes the X, Y, and Z coordinate values ​​in the data record. These coordinate values ​​are based on the workshop's global coordinate system, with the origin set at the workshop entrance. The node localization algorithm traverses the quadtree layer by layer, starting from the root node. It compares and calculates the spatial coordinates of the data record with the spatial boundaries of each node. Each quadtree node contains boundary information such as the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate. The algorithm determines whether the data record's coordinates fall within the boundaries of the current node. If the data coordinates are within the node boundaries and the node has child nodes, the algorithm continues to traverse the child nodes. If the data coordinates are within the node boundaries and the node is a leaf node, the node is determined to be the target data allocation node. If the data coordinates are not within the boundaries of any child nodes, the current node is the data allocation node. The positioning algorithm also needs to handle critical boundary situations. When the data coordinates happen to be located at the intersection of the boundaries of multiple nodes, a unique data allocation node is determined according to the preset boundary attribution rules. Each real-time data record in the workshop corresponds to a specific data allocation node, which records the reference information of all data records in the spatial area.

[0058] The three-dimensional state classification process performs multi-dimensional classification of data within a data allocation node based on process type number, equipment status number, and material status number. Process type numbers include 1 for frame processing, 2 for powertrain assembly, 3 for PBS scheduling, 4 for interior assembly, and 5 for chassis assembly. Equipment status numbers include 1 for operating, 2 for standby, and 3 for fault. Material status numbers include 1 for sufficient, 2 for insufficiency, and 3 for shortage. The classification algorithm traverses all data records within the data allocation node, extracting the process type number, equipment status number, and material status number for each record. This creates a three-dimensional classification matrix. The first dimension of the matrix corresponds to the process type, the second to the equipment status, and the third to the material status. Each element in the matrix contains a list of data records that meet the corresponding classification criteria. The classification process also addresses cases where a data record is missing a classification field. For records with missing equipment status numbers, inference is performed based on the equipment type and historical status data. For records with missing material status numbers, the status is inferred based on material inventory data and consumption rate. The classification result is process state classification data, which contains complete three-dimensional classification information and the corresponding data record index.

[0059] Equipment status statistics processing counts the process status classification data according to the operating state, standby state, and fault state. The operating state refers to the state in which the equipment is executing a production task, the standby state refers to the state in which the equipment is ready but not yet executing a task, and the fault state refers to the state in which the equipment has an abnormality and cannot work normally. The statistical algorithm traverses all data records in the process status classification data and groups and counts them according to the equipment status number. The operating status statistics include the number of records with the equipment status number 1 in all process types within the node, the standby status statistics include the number of records with the equipment status number 2, and the fault status statistics include the number of records with the equipment status number 3. The statistical process also needs to consider state changes within the time window. For equipment with frequent state changes, a time-weighted statistical method is used to calculate weighted statistical values ​​based on the duration of the equipment in each state. The time weight reflects the stability of the equipment in a specific state. The statistical results obtain equipment status statistics, which include the number of equipment in the operating state, the number of equipment in the standby state, the number of equipment in the fault state, and their corresponding time weight information.

[0060] Material status statistics are processed by categorizing process status data into sufficient, limited, and short-stock states. Sufficient status refers to a state where material inventory meets production needs and has a certain safety stock level. Limited status refers to a state where material inventory approaches the lower limit of the safety stock but can still maintain production. Short-stock status refers to a state where material inventory falls below the lower limit of the safety stock, potentially impacting production. The statistical algorithm filters and counts process status data based on material status numbers. Sufficient status statistics include the number of records with a material status number of 1, limited status statistics include the number of records with a material status number of 2, and short-stock statistics include the number of records with a material status number of 3. The statistical algorithm also dynamically adjusts based on material consumption rate and replenishment frequency. For materials with faster consumption rates, the thresholds for determining limited and short-stock states are appropriately increased. For materials with lower replenishment frequencies, the time window for status statistics is appropriately extended. The resulting material status statistics include the number of material types in sufficient, limited, and short-stock states, along with their corresponding inventory trend information.

[0061] The state aggregation matrix generation process constructs a matrix for each node based on the equipment state statistics and material state statistics. The state aggregation matrix is ​​a two-dimensional matrix structure, with rows corresponding to different process types and columns corresponding to different state combinations. State combinations include the Cartesian product of equipment state and material state. The aggregation algorithm first establishes a matrix framework, with the number of rows equal to the number of process types and the number of columns equal to the product of the number of equipment states and the number of material states. It then traverses the equipment state statistics and material state statistics, filling the corresponding matrix positions with statistical data. The matrix element values ​​represent the quantity statistics of the process type under a specific equipment state and material state combination. The aggregation process requires data consistency between different statistical results. Timestamp alignment is used to ensure that the equipment state statistics and material state statistics are from the same time window, and spatial range verification is used to ensure that the statistical data are from the same node range. The aggregation result is a process state aggregation matrix, which contains complete statistical information for each process type within the node under different state combinations.

[0062] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0063] Perform spatial region mapping processing on the display pixels in the digital twin 3D viewport through the viewport transformation matrix to obtain the spatial region corresponding to the pixel;

[0064] Perform fast query processing on the process status aggregation matrix according to the spatial area corresponding to the pixel to obtain the process status data within the pixel area;

[0065] Perform weighted calculation on the process status data within the pixel area based on the equipment status weight coefficient and the material status weight coefficient to obtain the process weighted aggregation value;

[0066] Input the equipment density factor and material density factor into the density correction algorithm to perform density correction calculation and obtain the density correction coefficient;

[0067] The pixel display value is comprehensively calculated and processed according to the process weighted aggregation value and the density correction coefficient to obtain the pixel comprehensive display value.

[0068] Specifically, the spatial region mapping process transforms the coordinates of the display pixels in the digital twin's 3D viewport using a viewport transformation matrix. This is a four-by-four mathematical transformation matrix that combines rotation, translation, and scaling operations to convert 2D screen pixel coordinates into 3D workshop space coordinates. The mapping algorithm first obtains the current viewport's observation parameters, including viewpoint position, viewing direction, field of view angle, near clipping plane distance, and far clipping plane distance. It then constructs a viewport transformation matrix, which incorporates a composite operation of perspective projection and view transformation. For each display pixel in the digital twin's 3D viewport, the mapping algorithm calculates the corresponding coordinate range of the pixel in the 3D workshop space based on the pixel's screen coordinates and depth information through the inverse transformation of the viewport transformation matrix. Due to the characteristics of perspective projection, a single pixel corresponds to a conical region in the workshop space. The intersection of this conical region and the workshop floor defines the spatial region corresponding to the pixel. The mapping process also needs to consider the neighborhood relationship of pixels. The spatial areas corresponding to adjacent pixels are continuous and overlapping. The algorithm calculates the precise boundaries of the spatial areas corresponding to pixels through pixel neighborhood interpolation to obtain the spatial areas corresponding to pixels. The spatial areas corresponding to each pixel contain boundary information such as the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate.

[0069] Fast query processing retrieves data from the process status aggregation matrix based on the spatial region corresponding to the pixel. The fast query algorithm utilizes the hierarchical nature of the quadtree index structure of the workshop process pixels to avoid the computational overhead of traversing all nodes. The query algorithm first locates the intersecting node set in the quadtree index structure based on the boundary coordinates of the spatial region corresponding to the pixel. Spatial intersection judgment includes three cases: point inclusion judgment, region overlap judgment, and region inclusion judgment. Point inclusion judgment verifies whether the vertex of the pixel spatial region falls within the boundary of the quadtree node. Region overlap judgment verifies whether there is an intersection between the pixel spatial region and the quadtree node boundary. Region inclusion judgment verifies whether the pixel spatial region is completely contained within the quadtree node boundary. The query algorithm traverses the intersecting node set, extracts the process status aggregation matrix stored in each node, and merges the aggregation matrices of multiple nodes. The merging calculation uses the matrix element-by-element addition method to obtain the comprehensive process status data within the pixel region. The query process also needs to deal with the problem of deduplication of data with overlapping boundaries. When the pixel space area spans multiple quadtree nodes, the data records on the boundary are avoided from repeated calculation. The algorithm uses the unique identifier of the data record to deduplicate and obtain the process status data within the pixel area, including statistical information of all process types in various state combinations in the spatial area corresponding to the pixel.

[0070] The weighted calculation process adjusts the weight of the process status data within the pixel area based on the equipment status weight coefficient and the material status weight coefficient. The equipment status weight coefficient reflects the degree of influence of different equipment status on production efficiency. The operating status weight coefficient is set to a higher value because the equipment in the operating state is directly involved in production activities. The standby status weight coefficient is set to a medium value because the equipment in the standby state has production capacity but is not working yet. The fault status weight coefficient is set to a lower value because the equipment in the fault state cannot participate in production activities. The material status weight coefficient reflects the degree of influence of different material status on production continuity. The sufficient status weight coefficient is set to a higher value because sufficient materials can ensure normal production. The tense status weight coefficient is set to a medium value because tense materials have potential supply risks. The shortage status weight coefficient is set to a lower value because the shortage directly affects the production progress. The weighted calculation algorithm traverses all matrix elements of the process status data within the pixel area, finds the corresponding weight coefficient according to the equipment status and material status corresponding to the element, multiplies the element value by the weight coefficient to obtain the weighted value, and the cumulative sum of all weighted values ​​constitutes the process weighted aggregation value, which reflects the comprehensive production status intensity in the spatial area corresponding to the pixel.

[0071] The density correction calculation process inputs the equipment density factor and material density factor into the density correction algorithm for normalization adjustment. The equipment density factor is equal to the total number of devices in the spatial area corresponding to the pixel divided by the area of ​​the spatial area, reflecting the equipment distribution density per unit area. The material density factor is equal to the total number of materials in the spatial area corresponding to the pixel divided by the area of ​​the spatial area, reflecting the material distribution density per unit area. The density correction algorithm first calculates the arithmetic mean of the equipment density factor and the material density factor as the comprehensive density factor, and then normalizes it according to the standard density value of the equipment and materials in the workshop. The normalized density correction coefficient is equal to the comprehensive density factor divided by the standard density value. When the normalized density correction coefficient is greater than 1, it means that the equipment and material density in the area is higher than the workshop average level. When the normalized density correction coefficient is less than 1, it means that the equipment and material density in the area is lower than the workshop average level. When the normalized density correction coefficient is equal to 1, it means that the equipment and material density in the area is close to the workshop average level. The density correction algorithm also needs to consider the spatial variability of density distribution. It performs smoothing processing through the density information of neighboring pixels to avoid sudden changes in the density correction coefficient and obtain the density correction coefficient. The density correction coefficient is used to adjust the spatial distribution uniformity of the process weighted aggregation value.

[0072] The pixel display value comprehensive calculation process is numerically synthesized based on the process weighted aggregation value and the density correction coefficient. The comprehensive calculation algorithm multiplies the process weighted aggregation value by the density correction coefficient to obtain the density-adjusted aggregation value, which is then standardized according to the dynamic range of the display value. The standardization process maps the density-adjusted aggregation value to a numerical range of 0 to 1. The mapping function uses linear or nonlinear transformation, and the appropriate mapping method is selected according to the visualization requirements. The comprehensive calculation also needs to consider the dynamic changes in the time dimension. For the pixel display values ​​of multiple consecutive time points, a time smoothing algorithm is used to reduce the sharp fluctuations in the display value. The time smoothing algorithm performs a weighted average based on the display values ​​of the current moment and the historical moment. The time weight decays as the time interval increases to obtain the pixel comprehensive display value. The pixel comprehensive display value reflects the comprehensive production status intensity of the workshop space area corresponding to the pixel.

[0073] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0074] The rendering detail level is graded according to the numerical range of the pixel comprehensive display value to obtain a high detail rendering level, a medium detail rendering level, and a low detail rendering level;

[0075] The workshop visualization content is constructed in a hierarchical structure based on the basic environment layer, equipment status layer, logistics track layer, and abnormal alarm layer, resulting in a four-layer rendering architecture.

[0076] Assign the three-dimensional models of the workshop structure, column racks, and workstation layout to the basic environment layer of the four-layer rendering architecture for static model rendering to obtain the basic environment rendering results;

[0077] Based on the pixel comprehensive display value, the operating status of the frame processing equipment, power assembly AGV, PBS scheduling system, and interior chassis assembly line is rendered using a thermal map to obtain the equipment status rendering results.

[0078] Multi-layer fusion rendering processing is performed based on the basic environment rendering results and the equipment status rendering results to obtain the three-dimensional visualization results of the automotive industry database.

[0079] Specifically, the rendering detail level grading process performs hierarchical classification based on the numerical range of the pixel comprehensive display value. The grading algorithm first determines the global numerical range of the pixel comprehensive display value, obtains the minimum and maximum values ​​by counting the comprehensive display values ​​of all pixels, and then divides the numerical range into three equal parts according to the preset threshold. The high detail rendering level corresponds to the pixel area with a pixel comprehensive display value greater than the high threshold. These areas have high production activity, high equipment density, and high process complexity. They need to display detailed equipment three-dimensional models, real-time operating parameters, material flow status and other fine information. The medium detail rendering level corresponds to the pixel area with a pixel comprehensive display value between the medium threshold and the high threshold. These areas have medium production activity and need to display medium-precision information such as equipment outline models, basic status information, and main process flows. The low detail rendering level corresponds to the pixel area with a pixel comprehensive display value less than the medium threshold. These areas have low production activity and low equipment density. They only need to display basic information such as color-coded status indicators and simplified equipment icons. The grading algorithm also needs to consider the spatial continuity of the pixel area to avoid drastic changes in the detail level of adjacent pixels. The detail level is adjusted for regional consistency through the spatial smoothing algorithm to obtain high detail rendering level, medium detail rendering level, and low detail rendering level with reasonable spatial distribution.

[0080] The layered architecture builds and processes the workshop visualization content, organizing it hierarchically into a basic environment layer, an equipment status layer, a logistics trajectory layer, and an anomaly alarm layer. The four-layer rendering architecture utilizes layered rendering graphics technology. Each rendering layer has its own independent render buffer and rendering pipeline, and inter-layer transparency is synthesized using an alpha blending algorithm. The basic environment layer, at the bottom of the rendering architecture, is responsible for rendering the workshop's static environmental elements, including building structures such as walls, floors, ceilings, columns, and gantry, as well as fixed facilities such as workstation platforms, conveyor lines, and operating tables. The basic environment layer utilizes static geometry rendering technology, with geometry data pre-loaded into video memory. Only transformations and lighting calculations are required during the rendering process. The equipment status layer, located above the basic environment layer, is responsible for rendering the workshop's dynamic equipment elements, including production equipment such as frame processing equipment, power assembly AGVs, PBS scheduling systems, and interior chassis assembly lines. The equipment status layer utilizes heat map rendering technology based on the pixel's integrated display value, mapping equipment status to varying colors and opacities. The logistics trajectory layer, located above the equipment status layer, is responsible for rendering dynamic logistics elements of the workshop, including dynamic trajectories such as material transfer routes, AGV driving trajectories, and EMS lifting routes. The logistics trajectory layer uses dynamic curve rendering technology, and trajectory data is dynamically updated based on real-time location information. The abnormality alarm layer, located at the top of the rendering architecture, is responsible for rendering abnormal status elements of the workshop, including alarm information such as equipment failure indicators, material shortage warnings, and process bottleneck prompts. The abnormality alarm layer uses highlight rendering technology to attract user attention through flashing animations, highlighted colors, and icon overlays.

[0081] Static model rendering processing assigns the three-dimensional models of the workshop building structure, column racks, and workstation layout to the basic environment layer for geometric rendering. The static model data comes from the workshop design drawings and three-dimensional modeling software. The model format adopts a standard three-dimensional geometric format, which contains geometric information such as vertex coordinates, patch indices, texture coordinates, and normal vectors. The rendering algorithm first performs a geometric transformation on the three-dimensional model, including the transformation from the model coordinate system to the world coordinate system, the transformation from the world coordinate system to the observation coordinate system, and the projection transformation from the observation coordinate system to the screen coordinate system. The transformation process uses matrix operations and is executed in parallel on the GPU through the vertex shader. The rendering algorithm then rasterizes the transformed geometry, converting the three-dimensional geometric patches into two-dimensional pixel fragments. The rasterization process includes basic graphics algorithms such as the triangle scan line algorithm, the depth buffer algorithm, and the texture mapping algorithm. The rendering algorithm finally shades the pixel fragments and calculates the color value of each pixel based on the lighting model. The lighting model includes light source types such as ambient light, directional light, and point light. The shading calculation takes into account factors such as material properties, surface normal vectors, and viewing direction. It is executed in parallel on the GPU through pixel shaders to obtain the basic environment rendering result. The basic environment rendering result contains a complete three-dimensional visual representation of the static environment of the workshop.

[0082] Heatmap rendering uses color mapping to represent the operating status of the frame processing equipment, power assembly AGV, PBS dispatch system, and interior chassis assembly line based on the pixel's integrated display value. Heatmaps are a data visualization technique that uses color variations to reflect the distribution of numerical values. The color mapping function maps the pixel's integrated display value to a preset color space. The rendering algorithm first establishes a color mapping table, which defines the correspondence between pixel integrated display values ​​and color values. The color space uses the HSV color model, where hue (H) represents color type, saturation (S) represents color purity, and value (V) represents color brightness. Low display values ​​correspond to cool blue tones, high display values ​​correspond to warm red tones, and medium display values ​​correspond to neutral green tones. The rendering algorithm then traverses all device objects in the device status layer, searches the color mapping table based on the integrated display value of the pixel region where the device is located, obtains the corresponding color value, and applies the color value to the material properties of the device object. The device object's geometry is rendered using the same geometric transformation and rasterization process as the base environment layer. The rendering algorithm also needs to process spatial interpolation and temporal interpolation of colors. Spatial interpolation ensures smooth color transitions between adjacent equipment objects, and temporal interpolation ensures continuous color changes when the equipment status changes. The interpolation algorithm uses bilinear interpolation and time-weighted averaging to obtain the equipment status rendering results. The equipment status rendering results include a heat map representation of the operating status of the workshop equipment.

[0083] Multi-layer fusion rendering combines images based on the base environment rendering results and the device status rendering results. Fusion rendering utilizes multi-pass rendering technology, generating independent color and depth buffers for each rendering layer. Inter-layer compositing is achieved through an alpha blending algorithm. The fusion algorithm first performs a depth test on the depth buffers of each rendering layer to determine the visible rendering layer at each pixel location. The depth test algorithm compares the depth values ​​of different rendering layers at the same pixel location. Pixel fragments with smaller depth values ​​are placed in the foreground, while those with larger depth values ​​are placed in the background. The fusion algorithm then performs alpha blending on the color buffers of the visible rendering layer. The alpha blending algorithm calculates a color value based on the transparency value of each rendering layer. The blending formula is: the foreground color multiplied by the foreground alpha value plus the background color multiplied by one minus the foreground alpha value. Blending is performed layer by layer, starting from the background and moving to the foreground. The fusion algorithm also handles anti-aliasing and post-processing effects. The anti-aliasing algorithm uses multi-sampling to reduce jagged edges on geometric edges. Post-processing effects include visual enhancements such as global illumination, shadow mapping, and depth of field blur. This results in a 3D visualization of the automotive industry database, which includes a complete visual representation of the workshop environment, equipment status, logistics trajectories, and anomaly alerts.

[0084] In a specific embodiment, the process of constructing a layered architecture for workshop visualization content based on the basic environment layer, the equipment status layer, the logistics track layer, and the abnormal alarm layer may specifically include the following steps:

[0085] Perform static element allocation processing on the basic environment layer to obtain the basic environment rendering layer including the workshop building structure, column racks, and workstation layout;

[0086] The equipment status layer is processed by heat mapping based on the pixel comprehensive display value to obtain the equipment status rendering layer including the operating status of the frame processing equipment, power subassembly AGV, PBS scheduling system, and interior chassis assembly line;

[0087] Based on the material flow path, AGV operation trajectory, and EMS lifting route, the logistics trajectory layer is dynamically configured and processed to obtain a logistics trajectory rendering layer containing logistics dynamic information;

[0088] Assign abnormal status information such as equipment failure, material shortage, and process bottleneck to the abnormal alarm layer for highlighting, and obtain an abnormal alarm rendering layer containing abnormal status identification;

[0089] Based on the hierarchical structure combination processing of the basic environment rendering layer, equipment status rendering layer, logistics trajectory rendering layer, and abnormal alarm rendering layer, a four-layer rendering architecture is obtained.

[0090] Specifically, the static element allocation process organizes the geometric data of the basic environment layer. Static elements include fixed buildings and facilities elements that do not change over time, such as the workshop building structure, columns and racks, and workstation layout. The allocation algorithm first extracts the geometric information of the workshop structure from the workshop three-dimensional digital model database. The workshop structure digital model contains the vertex coordinate data, surface topology relationships, and material attribute information of the walls, roof, and ground. The column and rack digital model contains the cross-sectional parameters, connection node coordinates, and beam layout of the steel structure columns. The workstation layout digital model contains the dimensions of the work surface, installation location coordinates, and equipment interface configuration. The allocation algorithm then groups and sorts the static elements according to the rendering priority. The rendering priority of the workshop shell structure is the highest because the shell structure constitutes the basic visual framework. The rendering priority of the columns and racks is second because the columns and racks provide structural support for the space. The rendering priority of the workstation layout is relatively low because the workstation layout belongs to the elements of the detail level. The allocation algorithm also needs to establish a spatial index for static elements. The spatial index uses a bounding box tree structure. For each static element, its minimum bounding box is calculated. The bounding box contains the minimum X coordinate, maximum X coordinate, minimum Y coordinate, maximum Y coordinate, minimum Z coordinate, and maximum Z coordinate. Then, a hierarchical bounding box tree is constructed according to spatial proximity. The bounding box tree is used to accelerate the frustum clipping and occlusion culling calculations during the rendering process, and a basic environment rendering layer containing the workshop building structure, column racks, and workstation layout is obtained. The basic environment rendering layer contains complete static geometric data and the corresponding spatial index structure.

[0091] Heatmap mapping color-codes the device status layer based on the pixel's integrated display value. Heatmap mapping is a visualization technique that converts numerical data into color representations. The mapping process achieves this correspondence through color space transformation. The mapping algorithm first establishes a mapping function between pixel integrated display values ​​and color values. This mapping function uses piecewise linear interpolation to divide the pixel integrated display value range into several subranges, each corresponding to a color gradient. Low display value ranges are mapped to a cool blue-to-cyan gradient, medium display value ranges are mapped to a neutral green-to-yellow gradient, and high display value ranges are mapped to a warm orange-to-red gradient. The mapping algorithm then traverses all equipment objects in the device status layer, including production equipment such as frame processing equipment, power assembly AGVs, PBS scheduling systems, and interior chassis assembly lines. Based on the equipment's spatial location, it finds the corresponding pixel integrated display value, calculates the color value of the equipment object using the mapping function, and assigns the color value to the material diffuse reflectance property of the equipment object. The mapping algorithm also needs to process the transparency calculation of equipment objects. The transparency is proportional to the pixel comprehensive display value. The higher the display value, the lower the transparency of the equipment object, which is more prominent visually. The lower the display value, the higher the transparency of the equipment object, which is relatively faded visually. The transparency is calculated using linear transformation. The transparency value is equal to the pixel comprehensive display value multiplied by the transparency coefficient plus the basic transparency. The equipment status rendering layer that includes the operating status of the frame processing equipment, power assembly AGV, PBS scheduling system, and interior chassis assembly line is obtained. The equipment status rendering layer contains the color and transparency information of all equipment objects.

[0092] Dynamic element configuration processing organizes trajectory data at the logistics trajectory layer based on material flow paths, AGV trajectory, and EMS lifting routes. Dynamic elements refer to time-varying motion trajectories and path information. The configuration process requires processing the temporal and spatial characteristics of the trajectories and their animation. The configuration algorithm first extracts material flow path information from logistics management data. Material flow paths include attributes such as the material's starting point coordinates, end point coordinates, intermediate node coordinates, flow direction, flow speed, and flow time. AGV trajectories include attributes such as the AGV starting position, target position, path node sequence, operating speed, turning radius, and stop time. EMS lifting routes include attributes such as the lifting start point, lifting end point, lifting height, operating trajectory, lifting speed, and safety distance. The configuration algorithm then performs spline curve fitting on the trajectory data. Spline curve fitting converts discrete path nodes into a continuous, smooth curve. The fitting algorithm uses a cubic B-spline curve. The control points of the spline curve correspond to the coordinates of the path nodes, the tangent direction of the spline curve corresponds to the direction of movement, and the curvature of the spline curve corresponds to the turning radius. The fitted spline curve ensures spatial continuity and smooth motion of the trajectory. The configuration algorithm also requires time parameterization of the trajectory. Time parameterization establishes a functional relationship between trajectory position and time, and calculates the timestamp corresponding to each position point on the trajectory based on the running speed and path length. Time parameterization supports real-time playback and time control of trajectory animation, and obtains a logistics trajectory rendering layer containing logistics dynamic information. The logistics trajectory rendering layer contains the geometric curves and time parameter information of all trajectories.

[0093] Highlighting assigns abnormal status information for equipment failures, material shortages, and process bottlenecks to the abnormality alert layer for visual reinforcement. Highlighting is a user interface technique that uses visual enhancements to attract user attention. The display algorithm first extracts abnormal status information from abnormality monitoring data. Equipment failure information includes attributes such as the faulty device number, fault type, fault location, fault time, and severity. Material shortage information includes attributes such as the shortage material number, shortage quantity, shortage location, warning time, and impact range. Process bottleneck information includes attributes such as the bottleneck process number, bottleneck location, bottleneck severity, duration, and upstream and downstream impacts. The display algorithm then selects an appropriate visual representation based on the abnormality type and severity. Equipment failures are indicated by a flashing red icon, whose size is proportional to the severity of the failure and the flashing frequency is proportional to the urgency of the failure. Material shortages are indicated by an orange warning icon, which is a triangular exclamation mark and whose transparency is inversely proportional to the severity of the shortage. Process bottlenecks are indicated by a yellow warning symbol, which is a circular obstruction mark and whose brightness is proportional to the severity of the bottleneck. The display algorithm also needs to process the spatial positioning and hierarchical coverage of exception information. The exception information is located and displayed according to the spatial location where the exception occurs. The positioning algorithm maps the coordinates of the exception position to the screen coordinates. The exception information is always displayed in the foreground of other rendering layers. Depth test disabling and Alpha blending are used to ensure that the exception information is not blocked by other geometric bodies. The exception alarm rendering layer containing the exception status identifier is obtained. The exception alarm rendering layer contains the visual representation and spatial positioning data of all exception information.

[0094] Hierarchical composition processing integrates the rendering pipeline based on the base environment rendering layer, device status rendering layer, logistics trajectory rendering layer, and anomaly alarm rendering layer. Hierarchical composition utilizes a multi-pass rendering architecture, with each rendering layer corresponding to an independent rendering pass consisting of components such as geometry data buffers, shader programs, and rendering state settings. The composition algorithm first establishes dependencies between the rendering layers. The base environment rendering layer serves as the underlying background layer and is independent of other rendering layers. The device status rendering layer relies on the depth buffer information provided by the base environment rendering layer, the logistics trajectory rendering layer relies on the color buffer information provided by the device status rendering layer, and the anomaly alarm rendering layer relies on the visual context information provided by all underlying rendering layers. The composition algorithm then executes the rendering operations of the rendering layers in the topological order of the dependencies. The rendering results of each rendering layer are output to the corresponding framebuffer object, which contains components such as the color buffer, depth buffer, and stencil buffer. Data transfer between rendering layers is achieved through texture objects, with the upper rendering layer using the framebuffer texture of the lower rendering layer as an input parameter. The combined algorithm finally performs the compositing operation of the rendering layer. The compositing operation uses Alpha blending technology. The blending equation calculates the pixel color value according to the transparency attribute of each rendering layer. The compositing result is output to the screen frame buffer for display, resulting in a four-layer rendering architecture. The four-layer rendering architecture includes a complete rendering pipeline and inter-layer dependencies.

[0095] In a specific embodiment, the process of executing the step of assigning the three-dimensional model of the workshop structure, column racks, and workstation layout to the basic environment layer of the four-layer rendering architecture for static model rendering processing may specifically include the following steps:

[0096] Perform lightweight processing on the three-dimensional digital model of the workshop building structure to obtain an optimized plant structure model;

[0097] Perform spatial coordinate calibration on the three-dimensional digital model of the column rack and workstation layout according to the process spatial positioning data to obtain the calibrated workshop layout model;

[0098] Based on the optimized plant structure model and the calibrated workshop layout model, spatial geometry combination processing is performed to obtain the overall three-dimensional model of the workshop;

[0099] The whole three-dimensional model of the workshop is grouped into rendering batches according to material type and geometric complexity to obtain grouped rendering batches;

[0100] The basic environment layer is statically rendered in batches according to the grouped rendering batches to obtain the basic environment rendering results.

[0101] Specifically, lightweighting optimizes the geometric complexity of the 3D digital model of the workshop building structure. Lightweighting is a geometric processing technique that reduces the data size of a 3D model. It reduces the model's storage and rendering overhead by reducing the number of polygonal patches, simplifying geometric details, and compressing texture data. The lightweighting algorithm first performs a geometric analysis of the digital model of the workshop building structure, which includes building components such as walls, roofs, beams, columns, doors, and windows. Each component is composed of triangular patches, and the patch data contains geometric properties such as vertex coordinates, normal vectors, and texture coordinates. The algorithm then calculates the visual importance of each patch based on factors such as patch size, normal vector change rate, and texture detail density. Larger patches are more visually important, patches with drastic normal vector changes represent distinct geometric features, and patches with rich texture detail contain important visual information. The lightweighting algorithm then performs patch simplification, which uses edge collapse. Edge collapse merges two adjacent vertices into a single vertex and deletes related patches. The simplification process prioritizes patches with low visual importance while retaining key geometric features with high visual importance. The algorithm also needs to perform texture compression processing. Texture compression converts high-resolution texture images into a compressed format. The compression algorithm uses lossy compression technology to reduce the size of texture data while maintaining visual quality, obtaining an optimized plant structure model. The optimized model maintains the main geometric features and visual effects while significantly reducing data storage and processing overhead.

[0102] Spatial coordinate calibration corrects the positional accuracy of the three-dimensional digital model of the column rack and workstation layout based on the process spatial positioning data. Spatial coordinate calibration is a geometric alignment technology that calculates coordinate transformation parameters and applies them to the three-dimensional model by comparing the differences between the theoretical design coordinates and the actual measured coordinates. The calibration algorithm first extracts the coordinates of the reference points in the process spatial positioning data. The reference points include the coordinates of the locating pin holes of the frame processing line workstations, the navigation marker coordinates of the powertrain assembly AGV path, and the positioning reference coordinates of the headstock storage position in the PBS area. These reference point coordinates are obtained through on-site measurement and have high spatial accuracy. The algorithm then searches for corresponding feature points in the three-dimensional digital model of the column rack and workstation layout. The feature points include geometric features such as the intersection of the column's central axis, the center of the locating hole of the workstation table, and the origin of the equipment installation reference plane. The feature point coordinates are calculated from the geometric data of the digital model. The calibration algorithm then calculates the spatial deviation between the coordinates of the reference points and the feature points. This deviation is calculated using the least squares method, establishing a coordinate correspondence between the reference and feature points. The algorithm then calculates the spatial transformation parameters that minimize the overall deviation. These transformation parameters include geometric transformation elements such as translation vectors, rotation matrices, and scaling factors. The algorithm then applies these calculated transformation parameters to all geometric vertices in the column rack and workstation layout. The transformation operation uses matrix operations, multiplying each vertex coordinate by the transformation matrix to obtain the calibrated coordinate position, resulting in a calibrated workshop layout model that remains consistent with the actual workshop spatial layout.

[0103] The spatial geometric combination process integrates the optimized factory building structure model and the calibrated workshop layout model to perform 3D model integration. Geometric combination is a 3D modeling technique that combines multiple independent 3D models into a unified overall model through Boolean operations, spatial splicing, and hierarchical assembly. The combination algorithm first establishes the spatial relationship between the models, with the factory building structure model serving as the spatial container and the workshop layout model as the internal filler. The two models are spatially aligned using a common coordinate system, which uses the workshop global coordinate system with the origin at the center of the workshop entrance, the X-axis running along the workshop length, the Y-axis running along the workshop width, and the Z-axis running along the workshop height. The algorithm then performs geometric interference detection to verify whether the geometry of the workshop layout model overlaps or conflicts with that of the factory building structure model. This detection algorithm uses a hierarchical approach combining bounding box intersection testing and precise geometric intersection testing. The bounding box test quickly eliminates obviously non-intersecting geometry, while the precise test verifies the true intersection of potentially intersecting geometry. The combination algorithm also needs to process the connections between models. These connections include structural relationships such as the fixed connection between columns and the factory frame, the installation connection between workstations and the floor, and the assembly connection between equipment and workstations. These connections are represented by constraints, which limit the relative position and orientation of the related geometric bodies. The algorithm finally performs a model merging operation, which combines multiple independent geometric bodies into a unified geometric data structure. This merged data structure contains information such as vertices, faces, materials, and textures for all geometric bodies, while maintaining the hierarchical relationships and connection constraints between the geometric bodies. This results in a comprehensive 3D model of the workshop, which contains a complete geometric representation and structural relationships of the workshop.

[0104] Render batch grouping optimizes the rendering of the entire workshop model by material type and geometric complexity. Render batch grouping is a graphics rendering optimization technique that groups geometric objects with similar rendering properties into the same batch, reducing rendering state switching overhead and improving rendering performance. The grouping algorithm first analyzes the material properties of each geometric object in the workshop model. Material properties include rendering parameters such as diffuse color, specular reflectance, transparency, and texture mapping. Geometries with the same or similar material properties are grouped together. This division into material groups reduces the number of material switches during the rendering process. The algorithm then evaluates the geometric complexity of each geometric object, measured by metrics such as the number of facets, number of vertices, and texture size. High-complexity geometry requires more rendering computational resources, while low-complexity geometry is relatively simple to render. Geometries of similar complexity are grouped together in the same complexity group. This grouping facilitates a balanced distribution of rendering resources. The grouping algorithm also considers the spatial distribution of geometric objects. Spatially adjacent geometric objects are often visible within the same view frustum. Grouping spatially adjacent geometries into the same spatial group facilitates optimization of view frustum culling and occlusion culling. Finally, the algorithm comprehensively considers factors such as material type, geometric complexity, and spatial distribution to establish a multi-dimensional grouping strategy. The grouping strategy adopts a hierarchical clustering method. First, coarse grouping is performed according to material type, then each material group is subdivided according to geometric complexity, and finally fine-tuning is performed according to spatial distribution within each subdivided group to obtain grouped rendering batches. Each rendering batch contains a collection of geometric bodies with similar rendering properties.

[0105] Batched static rendering executes GPU rendering of the base environment layer according to grouped rendering batches. Batched rendering is a graphics rendering organization technique that optimizes the GPU's parallel processing capabilities and memory access efficiency by submitting rendering commands in batches. The rendering algorithm first configures the rendering state for each rendering batch. The rendering state includes GPU rendering parameters such as shader programs, texture bindings, blending modes, and depth testing. Geometry within the same batch uses the same rendering state to avoid frequent state switching overhead. The algorithm then uploads the geometry data for each batch to the GPU memory. This geometry data includes GPU resources such as vertex buffer objects, index buffer objects, and texture objects. This data is uploaded in batches, reducing the number of communications between the CPU and GPU. The rendering algorithm then executes the batch rendering commands. Rendering commands are submitted to the GPU as draw calls. Each draw call processes all geometry in a rendering batch. The GPU executes the rendering pipeline stages—the vertex shader, geometry shader, and pixel shader—in parallel. The vertex shader handles geometric transformations and lighting calculations, while the pixel shader handles texture sampling and color blending. The algorithm also needs to post-process the rendering results. Post-processing includes image enhancement operations such as anti-aliasing, tone mapping, and gamma correction. The post-processing algorithm is executed in the fragment shader, and mathematical transformation and filtering are performed on the rendered pixel colors to obtain the basic environment rendering results. The rendering results contain a complete visual representation of the static environment of the workshop, and the rendering results are stored in the frame buffer object.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A digital twin-based comprehensive data processing method for automotive industry data visualization, characterized by: The method comprises: The three-dimensional space of the automobile assembly workshop is recursively divided into four parts by using the workshop process pixel quadtree index algorithm to obtain the workshop process pixel quadtree index structure. Perform process type tagging on multi-source heterogeneous data according to process identification mapping rules to obtain a real-time workshop data set containing process type identification; Allocating the workshop real-time data set to the workshop process pixel quadtree index structure according to spatial coordinates to perform process state aggregation processing to obtain a process state aggregation matrix; Performing weighted aggregation calculation processing on the process state aggregation matrix according to the viewport pixel mapping relationship to obtain a pixel comprehensive display value; Based on the pixel comprehensive display value, a multi-process parallel scene in a workshop is subjected to layered rendering processing to obtain a three-dimensional visualization result of an automobile industry database.

2. The method for comprehensive processing of automotive industry data visualization based on digital twins according to claim 1 is characterized in that: The three-dimensional space of the automobile assembly workshop is recursively divided into four parts by the workshop process pixel quadtree index algorithm to obtain the workshop process pixel quadtree index structure, including: The three-dimensional space of the automobile assembly workshop is divided into cubic space regions according to the process distribution characteristics to obtain the initial space segmentation units; Perform process element spatial positioning processing on the initial space segmentation unit according to the frame processing line station coordinates, the powertrain assembly AGV path nodes, and the PBS area head storage position to obtain process spatial positioning data; Performing recursive quad-segmentation calculation processing on the initial space segmentation unit based on the process space positioning data to obtain a quadtree node hierarchy structure; Allocating the process state aggregation matrix, the equipment quantity statistical vector, and the material quantity statistical vector to the quadtree node hierarchy for data storage processing to obtain the workshop process pixel quadtree index structure; A weight assignment process is performed on each node in the workshop process pixel quadtree index structure according to the process weight coefficient to obtain a weighted workshop process pixel quadtree index structure.

3. The method for comprehensive processing of automotive industry data visualization based on digital twins according to claim 1 is characterized in that: The process type tagging process is performed on the multi-source heterogeneous data according to the process identification mapping rule to obtain a workshop real-time data set containing the process type identification, including: Data is collected and processed from the frame processing line PLC, powertrain assembly AGV system, PBS regional scheduling system, interior main line MES, and chassis assembly line sensors through the OPC UA and Modbus protocols to obtain raw equipment data. Performing process type code assignment processing on the original equipment data according to the data source system to obtain equipment data with process identification; Performing data record structure processing on the equipment data with process identification based on timestamp, equipment number, and spatial coordinates to obtain standardized data records; Adding a data quality identifier to the standardized data record to perform data quality marking processing to obtain a complete data record; The complete data record is subjected to metadata association processing according to the data lineage relationship table to obtain the workshop real-time data set containing the process type identifier.

4. The method for comprehensive processing of automotive industry data visualization based on digital twins according to claim 2 is characterized in that: The step of allocating the workshop real-time data set to the workshop process pixel quadtree index structure according to spatial coordinates to perform process state aggregation processing to obtain a process state aggregation matrix includes: Performing node positioning processing on the weighted workshop process pixel quadtree index structure according to the spatial coordinate information in the workshop real-time data set to obtain a data allocation node; Performing three-dimensional state classification processing on the data in the data allocation node based on the process type number, equipment state number, and material state number to obtain process state classification data; Performing equipment status statistical processing on the process status classification data according to the operating state, standby state, and fault state to obtain equipment status statistical results; Perform material status statistical processing on the process status classification data according to the sufficient state, tight state, and shortage state to obtain material status statistical results; A state aggregation matrix is ​​generated for each node based on the equipment state statistical result and the material state statistical result to obtain the process state aggregation matrix.

5. The method for comprehensive processing of automotive industry data visualization based on digital twins according to claim 1 is characterized in that: The step of performing weighted aggregation calculation processing on the process state aggregation matrix according to the viewport pixel mapping relationship to obtain a pixel comprehensive display value includes: Perform spatial region mapping processing on the display pixels in the digital twin 3D viewport through the viewport transformation matrix to obtain the spatial region corresponding to the pixel; Performing a rapid query process on the process state aggregation matrix according to the spatial region corresponding to the pixel to obtain process state data within the pixel region; Performing weighted calculation processing on the process status data within the pixel area based on the equipment status weight coefficient and the material status weight coefficient to obtain a process weighted aggregation value; Input the equipment density factor and material density factor into the density correction algorithm to perform density correction calculation and obtain the density correction coefficient; A pixel display value is comprehensively calculated and processed according to the process weighted aggregation value and the density correction coefficient to obtain the pixel comprehensive display value.

6. The method for comprehensive processing of automotive industry data visualization based on digital twins according to claim 2 is characterized in that: The layered rendering process of the multi-process parallel scene of the workshop is performed based on the pixel comprehensive display value to obtain a three-dimensional visualization result of the automobile industry database, including: Grading the rendering detail level according to the numerical range of the pixel comprehensive display value to obtain a high detail rendering level, a medium detail rendering level, and a low detail rendering level; The workshop visualization content is constructed in a hierarchical structure based on the basic environment layer, equipment status layer, logistics track layer, and abnormal alarm layer, resulting in a four-layer rendering architecture. Assigning the three-dimensional models of the workshop structure, column racks, and workstation layout to the basic environment layer of the four-layer rendering architecture for static model rendering to obtain a basic environment rendering result; Performing heat map rendering processing on the operating status of the frame processing equipment, the power subassembly AGV, the PBS scheduling system, and the interior chassis assembly line according to the pixel comprehensive display value to obtain the equipment status rendering result; Multi-layer fusion rendering processing is performed based on the basic environment rendering result and the device status rendering result to obtain a three-dimensional visualization result of the automobile industry database.

7. The method for comprehensive processing of automotive industry data visualization based on digital twins according to claim 6 is characterized in that: The workshop visualization content is constructed in a hierarchical architecture based on the basic environment layer, equipment status layer, logistics track layer, and abnormal alarm layer, resulting in a four-layer rendering architecture, including: Perform static element allocation processing on the basic environment layer to obtain the basic environment rendering layer including the workshop building structure, column racks, and workstation layout; Perform heat map mapping on the equipment status layer according to the pixel comprehensive display value to obtain an equipment status rendering layer including the operating status of the frame processing equipment, the power subassembly AGV, the PBS scheduling system, and the interior chassis assembly line; Based on the material flow path, AGV operation trajectory, and EMS lifting route, the logistics trajectory layer is dynamically configured and processed to obtain a logistics trajectory rendering layer containing logistics dynamic information; Assign abnormal status information such as equipment failure, material shortage, and process bottleneck to the abnormal alarm layer for highlighting, and obtain an abnormal alarm rendering layer containing abnormal status identification; The four-layer rendering architecture is obtained by performing hierarchical structure combination processing based on the basic environment rendering layer, the device status rendering layer, the logistics trajectory rendering layer, and the abnormal alarm rendering layer.

8. The method for comprehensive processing of automotive industry data visualization based on digital twins according to claim 7 is characterized in that: The three-dimensional model of the workshop structure, column racks, and workstation layout is assigned to the basic environment layer of the four-layer rendering architecture for static model rendering processing to obtain a basic environment rendering result, including: Perform lightweight processing on the three-dimensional digital model of the workshop building structure to obtain an optimized building structure model; Perform spatial coordinate calibration on the three-dimensional digital model of the column rack and workstation layout according to the process spatial positioning data to obtain a calibrated workshop layout model; Performing spatial geometric combination processing based on the optimized plant structure model and the calibrated workshop layout model to obtain an overall three-dimensional model of the workshop; The whole three-dimensional model of the workshop is grouped into rendering batches according to material type and geometric complexity to obtain grouped rendering batches; The basic environment layer is subjected to batch static rendering processing according to the grouped rendering batches to obtain the basic environment rendering result.

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