{"id":7511,"date":"2026-07-04T02:03:30","date_gmt":"2026-07-03T18:03:30","guid":{"rendered":"https:\/\/silubaba.com.cn\/?p=7511"},"modified":"2026-07-04T02:03:32","modified_gmt":"2026-07-03T18:03:32","slug":"detailed-development-documentation-for-uav-image-data-processing-software-full-version","status":"publish","type":"post","link":"https:\/\/silubaba.com.cn\/?p=7511","title":{"rendered":"Detailed Development Documentation for UAV Image Data Processing Software (Full Version)"},"content":{"rendered":"\n<h1 class=\"wp-block-heading\"><\/h1>\n\n\n\n<ul class=\"wp-block-list\">\n<li><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Documentation version notes<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td class=\"has-text-align-left\" data-align=\"left\">Version number<\/td><td class=\"has-text-align-left\" data-align=\"left\">Update date<\/td><td class=\"has-text-align-left\" data-align=\"left\">Update Details<\/td><td class=\"has-text-align-left\" data-align=\"left\">Drafting unit<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">V1.0<\/td><td class=\"has-text-align-left\" data-align=\"left\">2026-07-02<\/td><td class=\"has-text-align-left\" data-align=\"left\">A complete draft of the development documentation includes system architecture, all functional modules, AI algorithms, cluster computing power, database, interfaces, deployment, and hardware adaptation<\/td><td class=\"has-text-align-left\" data-align=\"left\">Yiwu OOU Import &amp; Export Co., Ltd<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>1. Project Overview<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>1.1 Project Background<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With the rapid development of industries such as real-scene 3D China, natural resource surveys, territorial spatial planning, engineering surveying, and emergency aerial surveying, traditional drone modeling software faces pain points such as cumbersome operation, large amounts of manual intervention, weak large-scale image processing capabilities, limited computing power, unstable model accuracy, poor hardware compatibility, lack of intelligent processing capabilities, and lack of support for the latest 3D Gaussian real-scene reconstruction. To meet the needs of large-scale, high-precision, fully automated, and intelligent aerial survey production, our company has independently developed&nbsp;<strong>drone image data processing software<\/strong>&nbsp;to achieve one-stop fully automated 3D reconstruction of multi-source aerial survey data.<\/p>\n\n\n\n<!--more-->\n\n\n\n<p class=\"wp-block-paragraph\"><strong>1.2 Development Unit Information<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Developer: Yiwu OOu Import &amp; Export Co., Ltd<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Legal representative: Osman Tohsun<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Contact number: 086-15057908025<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>1.3 Software Positioning<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This software is a fully automated&nbsp;<strong>AI 3D reconstruction system<\/strong>&nbsp;based on Python, featuring a BS architecture desktop client + distributed cluster server architecture. It supports hardware access from multiple brands of drones and LiDAR, supports batch processing of ultra-large images at the 200,000 level, full-process AI intelligent processing, 3D Gaussian OPGS multi-level reconstruction, multi-mode spatial optimization, and all-type spatial reference adaptation. It fully meets the high-precision, large-volume, Normalized real-scene 3D production operations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>1.4 Development Goals<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enables multi-task parallel management of single projects, supporting multi-empty triple production, multiple reconstructions, and multiple products in synchronous production;<\/li>\n\n\n\n<li>Built a distributed parallel cluster computing power system to support ultra-large-scale processing of 200,000 images;<\/li>\n\n\n\n<li>Compatible with aerial, oblique, laser point clouds, video frame capture, multi-source data 3D reconstruction;<\/li>\n\n\n\n<li>Enables\u00a0<strong>full-process unattended and automatic airspace 3+ 2\/3D reconstruction<\/strong>\u00a0throughout the entire process of imaging, point cloud, and video frame capture;<\/li>\n\n\n\n<li>Enhance professional LAS point cloud processing, visual block segmentation, and custom parameter control capabilities;<\/li>\n\n\n\n<li>Achieves full-space reference adaptation, POS-free air three, 3D Gaussian real-scene reconstruction, point of contact linkage, and multi-mode air space optimization;<\/li>\n\n\n\n<li>Achieve performance targets for completing full-process reconstruction within 24 hours of 40,000 high-definition images from a 5-node cluster;<\/li>\n\n\n\n<li>Deeply integrated AI intelligent algorithms to achieve full-process intelligent upgrades;<\/li>\n\n\n\n<li>Compatible with mainstream aerial and lidar hardware devices from all brands on the market.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2. Overall System Architecture Design<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2.1 Architectural Patterns<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The system adopts&nbsp;<strong>front-end and back-end separation, microservices modularization, and a distributed cluster architecture<\/strong>, divided into five layers: hardware adaptation access layer, distributed server computing layer, AI algorithm core layer, BS desktop GUI interaction layer, and data storage layer. All modules are low-coupling and highly cohesive, supporting independent iteration, horizontal expansion, and functional plugging.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2.2 Detailed Technology Stack<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The&nbsp;<strong>backend core development language<\/strong>&nbsp;\ud83d\ude1b ython 3.10+<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Microservices framework<\/strong>: FastAPI (high-performance interface, asynchronous concurrency)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Task cluster scheduling<\/strong>: Celery + Redis + K8s lightweight cluster<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Geographic data kernels<\/strong>: GDAL, PyLas, OpenSfM, self-developed MESH networking, self-developed OPGS Gaussian reconstruction algorithm<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>AI deep learning frameworks<\/strong>\ud83d\ude1b yTorch, OpenCV<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Desktop client<\/strong>&nbsp;\ud83d\ude1b yWebView + Vue3 (BS desktop, lightweight and deploy-free)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Databases<\/strong>: MySQL 8.0 (structured data), MinIO (massive file object storage)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Communication protocols<\/strong>: HTTP\/HTTPS, WebSocket (real-time progress push)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2.3 Detailed Explanation of the Five-Layer Architecture<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2.3.1 Hardware Adaptation Access Layer<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Unified integration with multi-brand drone and lidar hardware devices, completing device protocol parsing, raw data normalization, standardized formats, and abnormal data filtering to block data differences across different hardware brands, providing standardized modeling data sources for upper-layer business.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2.3.2 Distributed Server Cluster Computing Layer<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The system&#8217;s core computing power carrier is responsible for task splitting, node scheduling, load balancing, parallel computing, abnormal retry, and breakpoint resume, carrying heavy computing tasks such as air triple encryption, mesh reconstruction, Gaussian reconstruction, point cloud processing, and coordinate transformation, supporting batch processing of ultra-large images at the 200,000 level.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2.3.3 Core layer of AI algorithms<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">An independent algorithm module spans the entire process of data preprocessing, spatial computation, model reconstruction, and quality inspection. Through deep learning models, it enables intelligent error correction, optimization, enhancement, and quality inspection, replacing traditional manual operations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2.3.4 BS Desktop GUI Interaction Layer<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Provides a visual operation interface, responsible for project management, block segmentation, parameter configuration, task monitoring, point record linkage, and result preview export, making operation lightweight, visual, and easy to use.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>2.3.5 Data Storage Layer<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Structured data and large file data are stored separately, ensuring read\/write speed, data security, traceability, and batch management.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>3. Overview of Core System Modules<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The system is divided into ten core functional modules, each independently packaged and coordinated:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Project tree multi-task management module<\/li>\n\n\n\n<li>Distributed cluster computing power scheduling module<\/li>\n\n\n\n<li>Multi-brand hardware adaptation and multi-source data access module<\/li>\n\n\n\n<li>AI fully automated data preprocessing module<\/li>\n\n\n\n<li>Professional laser point cloud processing module<\/li>\n\n\n\n<li>Visualize the three-block segmentation module<\/li>\n\n\n\n<li>2. 3D reconstruction parameter customization module<\/li>\n\n\n\n<li>Multi-type spatial reference adaptation modules<\/li>\n\n\n\n<li>3D Gauss OPGS multi-level reconstruction module<\/li>\n\n\n\n<li>The intelligent optimization of Kongsan and the linkage of the quality inspection module with Dianzhiji are also available<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4. Detailed development and design of each functional module (fully detailed and comprehensive)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.1 Project Tree Multi-Task Management Module (Meeting Requirements 1)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.1.1 Purpose of Module Development<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It addresses the problems of chaotic management in traditional software projects, single tasks for single projects, inability to run multiple projects in parallel, and difficulties in collecting results, achieving hierarchical, standardized, and multi-task management throughout the entire project lifecycle.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.1.2 Detailed Design of Core Functions<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The software displays a global visual engineering tree on the left side, using a four-level hierarchical structure of &#8220;Project-Engineering-Task-Deliverables,&#8221; with all data stored and categorized for display.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Project creation and management<\/strong>: supports manual new project creation, batch import, project renaming, project grouping, project archiving, project deletion, project backup and recovery, and supports custom project notes, project number, work area, work time, and person in charge information.<\/li>\n\n\n\n<li><strong>Single-project multi-task parallel processing<\/strong>: Within the same project, multiple groups of independent tasks can be created infinitely, including: multiple batches of empty triple encryption tasks, multiple sets of 2D orthographic reconstruction tasks, multiple sets of 3D MESH reconstruction tasks, multiple sets of 3D Gaussian reconstruction tasks, and multiple component export tasks. All tasks run independently, computing power allocated independently, data is isolated from each other, and there are no conflicts.<\/li>\n\n\n\n<li><strong>Task Full-State Control<\/strong>: Tasks include five major states: Queued, Calculating, Paused, Completed, and Failed. Supports manual pause, continue, terminate, restart, and retry failed tasks; Supports breakpoint resume; after server power outage or program shutdown, restarting can resume previous progress without recalculation.<\/li>\n\n\n\n<li><strong>Unified Results Aggregation Management<\/strong>: All aerial triangular outputs, orthophotos, 3D models, Gaussian models, point cloud results, and quality inspection reports under the same project are automatically aggregated into the corresponding project directory, supporting one-click preview, batch export, and categorized packaging.<\/li>\n\n\n\n<li><strong>Task Logs and Traceability<\/strong>: Each task automatically generates detailed operation logs, recording start and end times, computing power consumption, number of processed images, anomaly information, parameter configurations, supporting long-term traceability and troubleshooting.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.1.3 Module Technology Implementation<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The backend uses Python recursive tree structure algorithms to build hierarchical relationships in the project tree, storing the project&#8217;s unique ID, parent ID, task type, status, parameters, and path; Front-end dynamic rendering of tree components, supporting drag-and-drop sorting and shortcut right-click operations; Task queues are managed for multi-task concurrent scheduling through Celery.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.2 Large-scale Distributed Cluster Parallel Processing Module (Benchmarking Requirements 2, 12)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.2.1 Development Objectives<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Breaks through the traditional single-machine computing power limit, solves issues of lag, crashes, and excessive time consumption in large-scale image processing, enabling efficient, stable, and parallel computing of ultra-large volumes of aerial survey data, meeting the target of 200,000 image processing capacities and 5-node 24\/7 40,000 image reconstruction performance targets.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.2.2 Detailed Functional Design<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Multi-node cluster networking<\/strong>: Supports automatic networking of multiple servers\/computing power nodes. The system automatically identifies online nodes, offline nodes, and node computing loads, and displays real-time CPU, memory, disk, and network usage for each node.<\/li>\n\n\n\n<li><strong>Intelligent data sharding algorithm<\/strong>: For image datasets ranging from tens of thousands to 200,000 levels, the system automatically evenly slices data, distributing data evenly to all computing nodes for parallel computation, avoiding single-node overload.<\/li>\n\n\n\n<li><strong>Load balancing<\/strong>\u00a0scheduling: Dynamically assigns tasks to idle nodes; high-load nodes automatically reduce task allocation, low-load nodes automatically scale tasks, maximizing the use of cluster computing power.<\/li>\n\n\n\n<li><strong>Fault self-healing mechanism<\/strong>: If a node goes offline, crashes, or reports errors during operation, the system automatically migrates unfinished tasks from that node to another normal node and retries automatically, without affecting the overall project schedule.<\/li>\n\n\n\n<li><strong>Massive processing capability for 200,000 images<\/strong>: Supports importing 200,000 or more aerial survey images at a time, fully automating preprocessing, air three, and reconstruction processes, with no data limit lag or memory overflow.<\/li>\n\n\n\n<li><strong>Hard performance indicators implemented<\/strong>: In five standard computing power node cluster environments, for 40,000 images of 24 million pixels with terrestrial resolution better than 3cm HD, the entire process from data import, AI preprocessing, air encryption, 3D networking, texture mapping, to result planning\u00a0<strong>took \u2264 24 hours<\/strong>, with computing power utilization consistently above 75%.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.2.3 Technical Implementation<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Task distribution and scheduling are implemented based on Celery distributed task queues + Redis message middleware, K8s lightweight node state management, Python is used to write intelligent sharding and load balancing algorithms, and multi-process and multi-thread concurrency technologies enable batch processing of massive data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.3 Multi-Source Data and Multi-Brand Hardware Adaptation Reconstruction Module (Compared to Requirements 3 and 5)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.3.1 Development Objectives<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Solves issues of incompatibility between different brands of hardware data, single data sources, and ununified formats, enabling one-stop access and 3D reconstruction of aerial survey and LiDAR data across all categories.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.3.2 Detailed Hardware Adaptation Features<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The system has a built-in universal hardware protocol parsing and adaptation layer, requiring no third-party conversion software; it automatically identifies devices, parses raw data, and normalizes formats.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Compatible<\/strong>\u00a0with drone brands: DJI full series, Pegasus, XAG, Huace, Topcon, Southern Surveying and Mapping, and other mainstream surveying drones, automatically analyzing device images, POS attitude data, track data, and shooting parameters.<\/li>\n\n\n\n<li><strong>Compatible LiDAR equipment<\/strong>: DJI airborne LiDAR, Huace, Topcon, Reiger stand-mounted LiDAR, mobile vehicle\/airborne LiDAR, compatible with original device scan data.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.3.3 Detailed Multi-Source Data Processing Features<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Aerial Orthophotos<\/strong>: Supports single batch or batch import and reconstruction of conventional vertical aerial images;<\/li>\n\n\n\n<li><strong>Oblique Photography Images<\/strong>: Supports fully automatic matching and fusion reconstruction of five-view and multi-view oblique images;<\/li>\n\n\n\n<li><strong>Station-type laser point clouds<\/strong>: ground-mounted fixed laser scanning point cloud data processing and modeling;<\/li>\n\n\n\n<li><strong>Mobile laser point clouds<\/strong>: airborne and vehicle-mounted mobile scanned point cloud data processing;<\/li>\n\n\n\n<li><strong>Video frame capture images<\/strong>: supports automatic frame capture by drone aerial video, filtering valid frames, and batch modeling;<\/li>\n\n\n\n<li><strong>Point cloud format compatibility<\/strong>: natively supports *.las format, extended support for industry-standard point cloud formats such as laz and ply, providing a full set of functions for point cloud denoising, sparseness, registration, classification, and fusion.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.4 Fully Automatic AI Data Processing Module (\u2605 Core Benchmarking Requirement 4)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.4.1 Development Objectives<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Achieves fully&nbsp;<strong>automated unattended processing of three types of data sources: imaging, point clouds, and video frame capture<\/strong>, completely reducing manual intervention and achieving a fully automated closed loop from data import to output of results.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.4.2 Detailed Design of Fully Automatic Air Three-Processing Processing<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">No need for manual point selection, manual matching, or manual parameter adjustment\u2014the system&#8217;s AI fully automates the following process:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>AI automatically screens valid images, removes scrap films, and filters out blurry overexposed data;<\/li>\n\n\n\n<li>AI features are intelligently extracted and weak texture area features are enhanced;<\/li>\n\n\n\n<li>Fully automatic image matching, image pair generation, relative orientation;<\/li>\n\n\n\n<li>Fully automatic external azimuth element calculation and AI-based gross tolerance intelligent removal;<\/li>\n\n\n\n<li>Fully automatic free network adjustment, control network adjustment, and global optimization;<\/li>\n\n\n\n<li>Automatically generates high-precision aerial 3D results, sparse point clouds, and dense point clouds.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.4.3 Detailed Design of Fully Automatic 2D and 3D Reconstruction<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">After the results of the Flight 3 are generated, the system automatically triggers the reconstruction process, with the entire process unattended:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Fully automatic 2D orthophoto stitching, color leveling, cropping, and correction;<\/li>\n\n\n\n<li>Fully automatic 3D mesh networking, topology generation, and texture mapping;<\/li>\n\n\n\n<li>Fully automatic model void repair, twist repair, edge optimization;<\/li>\n\n\n\n<li>Fully automated results are organized, format output, and file archiving.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.5 Spatial Visualization Block Segmentation Module (Compared to Requirement 6)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.5.1 Detailed functional design<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">During the Aerospace III operation phase, it provides a visual 2D base map interactive interface, supports fine-grained segmentation management of survey areas, and solves issues such as uneven computing power across large survey areas, poor local accuracy, and modeling failures.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Base maps automatically load full-area aerial coverage and visualize the distribution of images;<\/li>\n\n\n\n<li>Supports rectangular box selection and freely drawing irregular polygon blocks;<\/li>\n\n\n\n<li>Supports multi-block splitting, block merging, block deletion, and block renaming;<\/li>\n\n\n\n<li>Each block can independently allocate computing power, start empty space independently, rebuild independently, and inspect quality independently;<\/li>\n\n\n\n<li>Visualizes the number of images, coverage area, processing progress, and completion status for each block.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.6 Rebuilding Parameter Custom Configuration Module (\u2605 Benchmark Requirements 7)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.6.1 Detailed Parameter Design<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The system provides a visual parameter panel, with all parameters visually adjustable, supporting custom parameter template saving and one-click reuse.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>2D networking parameters<\/strong>: image sampling accuracy, pixel compression ratio, splicing smoothness, chromatic aberration equalization intensity, edge cropping threshold, feathering range, and customizable resolution;<\/li>\n\n\n\n<li><strong>3D MESH mesh parameters<\/strong>: mesh density level, maximum and minimum side length of triangular faces, number of topology iterations, mesh optimization strength, detail retention coefficient;<\/li>\n\n\n\n<li><strong>Model simplification adjustment<\/strong>: supports 10%-99% custom simplification scale, multi-level refined levels, intelligently distinguishes buildings, terrain, and vegetation areas, retains key details during simplification, removes redundant grids, and adapts to high-precision delivery, lightweight display, web loading, and simulation demonstration scenarios.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.7 Multi-Space Reference Adaptation Module (\u2605 Matching Requirements 8)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.7.1 Detailed Functional Design<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Fully covering all coordinate application scenarios in the surveying industry, solving challenges in modeling special POS-less systems, custom coordinate systems, and local independent coordinate systems.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Standard geospatial references<\/strong>: built-in national standard coordinate systems such as CGCS2000, WGS84, Beijing 54, Xi&#8217;an 80, and various projection zones;<\/li>\n\n\n\n<li><strong>Custom space reference<\/strong>: supports manual input of four-parameter, seven-parameter, projection, and ellipsoid parameters, and supports importing PRJ parameter files to customize coordinate systems;<\/li>\n\n\n\n<li><strong>Local Space Reference<\/strong>: Supports custom origin coordinates, azimuth angles, and scales, creating local independent coordinate systems, suitable for construction sites and small-scale temporary surveying scenarios;<\/li>\n\n\n\n<li><strong>POS-free imaging air three:<\/strong>\u00a0In the absence of drone posture or GPS positioning data, AI feature matching + free network adjustment algorithms automatically complete air three-dimensional processing and model reconstruction, adapting to scenarios of outdated data and missing field data.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.8 3D Gaussian OPGS Multi-Level Reconstruction Module (\u2605 Compared to Requirement 9)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.8.1 Detailed module design<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It features a self-developed&nbsp;<strong>3D Gaussian Reconstruction (OPGS) algorithm<\/strong>, which distinguishes it from traditional triangular mesh models by enabling hyper-realistic real-scene 3D modeling, compensating for the shortcomings of traditional MESH models such as texture distortion, missing details, and stiff edges.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Based on dense point clouds + image texture features in the air-3, it automatically generates Gaussian splash 3D scenes;<\/li>\n\n\n\n<li>Supports\u00a0<strong>multi-level precision output<\/strong>: ultra-fine, high-precision, standard, and lightweight four layers;<\/li>\n\n\n\n<li>AI intelligently optimizes Gaussian point distribution density, retains details in high-density areas, and streamlines redundant data in low-density areas;<\/li>\n\n\n\n<li>Gauss achievements support 3D preview, roaming, measurement, and export, adapting to digital twins, real-scene displays, and high-precision acceptance scenarios;<\/li>\n\n\n\n<li>Supports bidirectional output of Gauss models and traditional MESH models to meet different delivery standards.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.9 Dianzhiji Photo Linkage Module (\u2605 Matching Requirements 10)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.9.1 Detailed Functional Design<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">An intelligent linkage function specially designed for surveying accuracy verification, field data archiving, and result acceptance, enabling one-click visual verification of control points.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Supports batch import of coordinate data from field control points and connection points;<\/li>\n\n\n\n<li>Supports batch uploading of real-scene photos of corresponding points, with automatic binding of point IDs;<\/li>\n\n\n\n<li>When operators switch any control point in the control point list or 3D scene, the system automatically displays a photo of the corresponding point\u00a0<strong>in linkage<\/strong>;<\/li>\n\n\n\n<li>Simultaneously displays point coordinates, residuals, deviation values, field notes, and collection time;<\/li>\n\n\n\n<li>Supports photo zoom, preview, zoom, export, replace, and rebinding;<\/li>\n\n\n\n<li>Supports one-click archiving and export of the full set of control point data, for project acceptance archiving.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.10 Multi-mode Airspace Optimization Module (\u2605 Compared to Requirement 11)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.10.1 Detailed Algorithm Design (4 Optimization Modes to Exceed Requirements)<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Free-network edge joint optimization<\/strong>: For large-scale aerial survey data without control points, global iterative adjustment is used to unify the overall posture of blocks, eliminating model stretching, twisting, and edge gaps, ensuring large-scale model integrity and flatness.<\/li>\n\n\n\n<li><strong>Control network edge optimization<\/strong>: Relying on field-measured control points, constrained global coordinate datums, corrected system errors, greatly improved absolute positioning accuracy, and met surveying acceptance standards.<\/li>\n\n\n\n<li><strong>Connection control point optimization<\/strong>: Performs local fine adjustments for block connection points with the same name, eliminating texture faults, local misalignments, and stitching traces, and optimizing model detail connection effects.<\/li>\n\n\n\n<li><strong>AI Intelligent Global Optimization<\/strong>: Self-developed new optimization mode, AI automatically detects weak textures, backlighting, shadows, and complex terrain areas, adaptively adjusts adjustment weights, and intelligently corrects point deviations and modeling flaws.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.11 Detailed design of the full module of AI intelligent algorithms<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The AI module of this software is self-developed based on the Python PyTorch deep learning framework, fully embedded in business processes, representing the software&#8217;s core differentiating advantage.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AI intelligent data cleaning model<\/strong>: trained binary classification filtering model, automatically identifies blurry, overexposed, underexposed, duplicated, and displacement invalid images, automatically filters point cloud discrete noise and anomalies, and purifies modeling data sources.<\/li>\n\n\n\n<li><strong>AI Weak Texture Feature Enhancement Model<\/strong>: Targets low-feature areas such as water surfaces, grasslands, snow, and bare ground, using image feature enhancement algorithms to strengthen details, increase the number of feature matches, and solve issues such as airspace failure, model voids, and sparse point positions.<\/li>\n\n\n\n<li><strong>AI Void Three-Gross Error Rejection Model<\/strong>: Uses deep learning to identify abnormal matching image pairs, incorrect positions, and gross errors, automatically removes interference data, improves adjustment accuracy, and reduces manual error correction costs by over 80%.<\/li>\n\n\n\n<li><strong>AI Model Defect Repair Model<\/strong>: Intelligently detects model voids, distortions, texture misalignments, and stretching deformations, automatically repairs defect areas, and improves overall model quality.<\/li>\n\n\n\n<li><strong>AI adaptive parameter recommendation model<\/strong>: automatically recommends the optimal empty space based on image resolution, shooting angle, terrain scenario, and lighting conditions. 3. Reconstruction, model simplification of parameters, lowering the barrier to use.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>4.12 Detailed Feature Design of BS Desktop GUI Client<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The client is packaged with lightweight desktop programs based on PyWebView, which is green, installation-free, cross-system compatible, and all operations are visualized and user-friendly.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The project tree has a visual management interface with clear hierarchical levels and convenient operation;<\/li>\n\n\n\n<li>Real-time visual display of task progress, with WebSocket pushing progress percentages in real time;<\/li>\n\n\n\n<li>Empty three blocks with a visual segmentation, editing, and management interface;<\/li>\n\n\n\n<li>Visual configuration of reconstruction parameters, template saving, one-click application;<\/li>\n\n\n\n<li>Spatial reference visualization selection and custom parameter input interface;<\/li>\n\n\n\n<li>Control point management + Dianzhiji photo linkage preview interface;<\/li>\n\n\n\n<li>Real-time preview, roaming, measurement, and sectioning of 3D models and Gaussian results;<\/li>\n\n\n\n<li>Batch output export, format conversion, and file packaging functions;<\/li>\n\n\n\n<li>System logs, operating status, and computing node monitoring panels.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>5. Detailed database design<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>5.1 Project Information Table<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Store project ID, project name, project number, creation time, responsible person, work area, coordinate system, project status, and remarks information.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>5.2 Task Information Table<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Store task ID, affiliated project ID, task type (naked 3D\/2D\/3D\/Gaussian\/point cloud processing), task status, parameter configuration, start time, end time, computing node, runlog, and exception information.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>5.3 Control Point Information Table<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Stores control point ID, point name, X\/Y\/Z coordinates, residuals, point memory, photo storage path, associated block, and collected information.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>5.4 System Parameter Configuration Table<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Stores user-defined modeling parameters, coordinate parameters, simplified parameters, AI switch parameters, and template configurations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>5.5 Hardware Adaptation Parameter Table<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Stores data analysis rules and parameter adaptation templates for various brands of drones and LiDAR to ensure hardware automatic recognition and adaptation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>6. Interface Design Description<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>6.1 Hardware Data Access Interface (Detailed Requirements)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface Positioning<\/strong>: The system&#8217;s underlying hardware adapts to a universal core interface, serving as the unified entry point for data from all aerial survey and lidar devices. It enables unified access, parsing, verification, and normalization of raw data from multiple brands and types of hardware, thoroughly shielding data format differences among different manufacturers. This provides standardized, high-quality, and directly computable data sources for upper-layer air 3, reconstruction, and AI preprocessing services, supporting multi-device hybrid operations and large-scale automated data access scenarios.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Compatible hardware<\/strong>&nbsp;range: Fully compatible with mainstream surveying drones such as DJI, Pegasus, XAG, Huace, Topcon, and Southern Surveying and Mapping; Compatible with DJI onboard LiDAR, Huace, Topcom, and Reiger stationary\/mobile LiDAR equipment, supporting aerial imagery, oblique image, airborne POS tracking, laser point clouds, aerial video, and all types of raw data access.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Detailed input parameters<\/strong>: hardware device model code, raw data file path\/data stream, image\/JPG\/TIF raw file, *.las point cloud original file, device POS posture data, altitude focal length acquisition parameters, video source file, device acquisition time, field aviation parameter.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Detailed output parameters<\/strong>: standardized organized imaging dataset, normalized POS posture parameter files, standard LAS format point cloud data, device adaptation verification reports, data compliance determination results, anomaly data lists, and a collection of modelable valid data sources.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core functional requirements<\/strong>: 1. Automatic hardware recognition, eliminating the need for manual device model selection; the system automatically matches device parsing rules based on data features; 2. Multi-format compatibility parsing, automatically repairing non-standard raw data and filling in missing parameters; 3. Fully automatic data normalization, unified resolution, pixel format, and coordinate coding to eliminate device data discrepancies; 4. Intelligent dirty data filtering, automatically removing damaged, black screen, overexposure, duplicate, and invalid data shifted; 5. Supports batch parallel access, simultaneous multi-device data imports without conflict, suitable for 200,000-level ultra-large data preprocessing scenarios; 6. Retains original collection parameters, traces the source throughout the process, and does not lose original field information.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Exception handling mechanism<\/strong>: For abnormalities such as data corruption, formatting errors, missing parameters, or device misrecognition, automatic hierarchical alerts are generated, abnormal logs are generated, invalid data is isolated without affecting normal data processing workflows, and manual secondary review and correction is supported.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface Purpose<\/strong>: Integrates with mainstream drones and LiDAR hardware on the market, receives raw data such as images, POS tracks, laser point clouds, and imaging parameters output from these devices, performs data parsing, format verification, and normalization, shields data differences across different hardware brands, and provides standardized data sources for upper-layer modeling services.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Input parameters<\/strong>: hardware raw data stream, device model identifier, original file path, data format suffix, device acquisition parameters (altitude, focal length, scanning angle, positioning information).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Output parameters<\/strong>: standardized imaging dataset, normalized POS posture file, standard LAS point cloud data, device adaptation log, data compliance verification results.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core business needs<\/strong>: automatic identification of hardware devices from multiple brands such as DJI, Pegasus, XAG, Huace, Topcon, etc.; Automatically parses non-standard raw data and converts it uniformly into a system-wide modeling format; Automatically filters out damaged, missing, and abnormal hardware data; Supports batch access to multi-device parallel data, ensuring adaptation to multi-hardware hybrid work scenarios.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>6.2 Engineering Task Management Interface (Detailed Requirements)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface Positioning<\/strong>: Core scheduling interface for system business processes, fully responsible for project lifecycle management and task queue scheduling, supporting front-end engineering tree visualization, multi-task parallelism, task status control, achievement aggregation, and log traceability. It is the core supporting interface for achieving multi-empty triple operations for single projects, multiple reconstructions, and multi-product synchronized operations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Enter parameter details<\/strong>: unique project ID, project name, project number, work area notes, responsible person information, task type (empty 3 encryption\/2D orthographic reconstruction\/3D MESH reconstruction\/3D Gaussian OPGS reconstruction\/point cloud processing\/result export), custom modeling parameters, task priority, operation instructions (add\/modify\/delete\/pause\/resume\/terminate\/restart\/archive\/backup).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Output parameter details<\/strong>: operation success\/failure receipts, project structured information, task queue number, task operation status identification, task progress information, output file collection path, operation log, engineering backup files, task exception details.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core functional requirements<\/strong>: 1. Complete project CRUD capabilities, supporting create, edit, rename, group, archiving, delete, backup, and restore; 2. Parallel scheduling of multiple tasks for a single project, with multiple types of tasks running independently within the same project, data isolation, independent allocation of computing power, and no interference between each other; 3. Full-state task control, supporting switching between queue, run, pause, completion, and failure in all states; 4. Core feature of breakpoint resume: after power outage, crash, shutdown, or reboot, historical progress is automatically resumed without recalculation; 5. Automatic achievement aggregation, unified archiving and classification of all project task results, supporting traceability and retrieval; 6. Batch task management, supports batch creation, batch start\/stop, batch archiving, suitable for large-scale project production.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Exception handling mechanism<\/strong>: Automatically intercepts abnormalities such as illegal task parameters, duplicate project creations, task conflicts, and insufficient disk space, returns standardized error codes and prompts, records the entire log, and supports rapid troubleshooting.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface purpose<\/strong>: supports system engineering trees and full lifecycle task management, enabling the addition, editing, deletion, status updates, batch scheduling, and result binding of projects and tasks. It is the core foundational interface of software business processes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Input parameters<\/strong>: project ID, project name, project notes, task type (empty 3D\/2D reconstruction\/3D reconstruction\/Gaussian reconstruction\/point cloud processing), task parameters, task status, operation commands (add\/modify\/delete\/pause\/restart).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Output parameters<\/strong>: engineering operation status receipt, task creation results, task queue number, task operation status, project results collection path, operation log records.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core business requirements<\/strong>: supports single-project multi-task parallel creation and independent scheduling, with task data isolated; Supports batch import, archiving, backup and recovery of projects; Supports resume of task interruptions, abnormal retrys, and real-time status updates; Supports front-end engineering tree visualization and backend automated task scheduling.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>6.3 Cluster Computing Power Scheduling Interface (Detailed Requirements)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface positioning<\/strong>: The core management interface for distributed cluster computing power, responsible for multi-node networking, intelligent task sharding, load balancing, fault migration, and coordinated allocation of computing resources. It is the core computing foundation interface supporting large-scale processing of 200,000 images and efficient reconstruction of 40,000 images across 5 nodes 24 hours a day.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Detailed input parameters<\/strong>: unique cluster node ID, node IP and port, real-time node load data (CPU usage, memory usage, disk IO, network bandwidth), image dataset to be processed, task hash weight, task priority, shard granularity parameters, node start\/stop status.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Detailed output parameters<\/strong>: node online\/offline status, task sharding distribution list, node computing power allocation ratio, real-time load balancing data, task-bound node information, computing power utilization statistics, node anomaly alerts, task migration receipts.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core functional requirements<\/strong>: 1. Cluster nodes are fully automated for network detection, with real-time checks of all computing nodes online and faulty nodes automatically eliminated; 2. Ultra-large volumes of data are intelligently divided and evenly divided, automatically splitting images from tens of thousands to 200,000 to prevent single-node overload and jamming; 3. Dynamic load balancing algorithm: idle nodes automatically scale up tasks, high-load nodes automatically limit rates and reduce load load, maximizing cluster computing power utilization; 4. Fault self-healing and task hot migration: if nodes crash, go offline, or report errors, unfinished tasks are automatically migrated to normal nodes for continued computation, without interrupting project progress; 5. Computing power priority scheduling, supporting the priority occupation of computing power resources for important project tasks; 6. Statistical analysis of computing power data, real-time output of overall cluster utilization, single-node load, and task time statistics to support operation and maintenance management.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Exception handling mechanism<\/strong>: Real-time alerts for abnormalities such as node offline status, computing power overload, sharding failure, and task blocking, automatically triggering task retries and migrations, recording cluster operation fault logs, and ensuring stable batch computing 24\u00d777.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface purpose<\/strong>: Responsible for unified management and control of all computing nodes in distributed clusters, enabling node status monitoring, intelligent task distribution, dynamic load balancing, and fault task migration, ensuring stable batch processing of 200,000-level ultra-large images, and meeting the performance indicators of 5-node clusters.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Input parameters<\/strong>: cluster node ID, node hardware load data (CPU\/memory\/disk\/network), pending task dataset, task priority, computing power allocation ratio.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Output parameters<\/strong>: node online status, task sharding distribution results, load balancing ratio, task operation node binding information, node abnormality alarm information, and computing power utilization statistics.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core business requirements<\/strong>: Real-time polling monitoring of all computing node operating status; Automatically equalizes and intelligently distributes large batches of image data to idle nodes; Dynamically adjust node load to avoid single-node overload; Automatically migrates tasks left unfinished during node downtime\/offline to ensure uninterrupted projects; Supports customizable computing power priority and prioritizes scheduling of important tasks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>6.4 AI Algorithm Call Interface (Detailed Requirements)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface positioning<\/strong>: Full-process AI intelligence capability unifies unified interfaces, modularly integrates all deep learning algorithms in the software, providing standardized AI calling capabilities for data preprocessing, spatial optimization, model reconstruction, and quality inspection, enabling the entire traditional modeling process to intelligently replace manual operations. It serves as the software&#8217;s core differentiated intelligent capability support interface.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Detailed input parameters<\/strong>: raw image dataset, raw LAS point cloud data, sparse\/dense point cloud results for aerial 3, 3D mesh model results, modeling scenario types (city\/terrain\/water area\/vegetation zones), various AI function switches, algorithm accuracy levels, adjustment optimization parameters, and model repair thresholds.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Detailed output parameters<\/strong>: valid dataset after AI cleaning, feature-enhanced images, standard point cloud after denoise, high-precision empty three-point position after removing rough deviations, flawless 3D model after repair, AI adaptive optimal modeling parameters, intelligent quality inspection reports, and statistical logs of algorithm operation time and accuracy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core functional requirements<\/strong>: 1. Modular unified calls, supporting independent calls of single-class AI algorithms as well as fully automatic linkage of multiple algorithm combinations; 2. Comprehensive AI capabilities, including data cleaning, weak texture enhancement, point cloud denoising, empty and coarse difference removal, model void distortion repair, adaptive parameter recommendation, and intelligent quality inspection; 3. Asynchronous, non-blocking operations: AI backend runs without occupying front-end operation resources or blocking main thread tasks; 4. Intelligent scene adaptation, automatically adjusting algorithm weights based on terrain, lighting, and image quality; 5. Algorithms can be iteratively upgraded, interfaces compatible with newly added AI models, offering strong scalability; 6. Fully controllable precision throughout the process, supporting user-defined AI processing accuracy levels, adapting to dual scenarios of rapid production and high-precision acceptance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Exception handling mechanism<\/strong>: automatically intercepts data that does not meet AI computation conditions, model inference anomalies, parameter configuration errors, etc., returning the reason for AI processing failure, preserving the original data without damage, and supporting recomputation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface purpose<\/strong>: Unify all AI intelligent algorithm modules in the encapsulated system, provide standardized access points, support intelligent operations for data preprocessing, air optimization (EMPS), model repair, and quality inspection, and achieve modular, reusable, and iterative AI capabilities.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Input parameters<\/strong>: raw image dataset, raw point cloud data, preliminary results of Aerial Three, type of modeling scenario, AI function switch, algorithm accuracy parameters.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Output parameters<\/strong>: valid data after cleaning, enhanced feature images, empty three-point positions after removing rough deviations, post-remediation model, AI quality inspection report, algorithm runtime log.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core business requirements<\/strong>: supports one-click call to AI data cleaning, weak texture enhancement, naked three gross error removal, model defect repair, adaptive parameter recommendation, and all algorithms; Supports independent call of single algorithms and linked call of multiple algorithms; Algorithm execution does not block the main thread and supports backend asynchronous computation; AI model parameters can be iteratively upgraded according to project requirements, offering strong compatibility.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>6.5 Spatial Reference Conversion Interface (Detailed Requirements Description)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface Positioning<\/strong>: The surveying coordinate system is uniformly adapted to core interfaces, covering national standard coordinate systems, custom coordinate systems, local independent coordinate systems, and POS-free modeling coordinate solution scenarios. This enables unified, precise conversion of all modeling data coordinates and parameters for parameter adaptation, ensuring the software&#8217;s multi-scenario, no-loop surveying coordinate production capability, and multi-space reference adaptation for benchmarking bidding with core parameters.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Detailed input parameters<\/strong>: raw spatial reference code, target space reference code, four-parameter\/seven-parameter conversion values, custom ellipsoid parameters, projection index zone parameters, external PRJ coordinate parameter files, POS modeling identifiers, raw image point data, control point coordinate data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Detailed output parameters<\/strong>: converted standardized coordinate dataset, coordinate adaptation verification report, coordinate deviation accuracy statistics, POS-free network adjustment results, coordinate system binding engineering parameters, projection conversion log files.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core functional requirements<\/strong>: 1. One-click adaptation of national standard coordinate systems, built-in full range of coordinate systems including CGCS2000, WGS84, Beijing 54, Xi&#8217;an 80, and band projection; 2. Fully supports custom space references, with quick adaptation by manually entering conversion parameters or importing PRJ files; 3. Supports automatic construction and adaptation of local independent coordinate systems, meeting temporary site surveying scenarios; 4. Fully automatic coordinate calculation for POS images, relying on free network adjustment to complete positioning data modeling; 5. Controllable coordinate accuracy throughout the entire process, with conversion deviations strictly meeting surveying industry standards, no stretching, no offset, no distortion; 6. Coordinate parameters are globally bound to the project, with unified coordinate datums for results, adapting to later splicing, archiving, and acceptance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Exception handling mechanism<\/strong>: automatically prompts abnormalities such as parameter input errors, coordinate system mismatches, and conversion limit exceedances; illegal coordinate conversions are prohibited, original coordinate data is preserved, and the security and compliance of delivered coordinates are ensured.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface Purpose<\/strong>: Covers full-scenario coordinate adaptation needs for surveying and mapping, enabling definition, conversion, and adaptation of multiple types of spatial references, supporting standard coordinate systems, custom coordinate systems, local coordinate systems, and coordinate solution services for modeling without POS air 3.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Input parameters<\/strong>: Original coordinate system, target coordinate system, four\/seven parameters, PRJ projection file, no POS label, original coordinates of image points.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Output parameters<\/strong>: converted standard coordinate data, coordinate system adaptation results, coordinate accuracy verification report, and adjustment coordinate results without POS free network.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core business needs<\/strong>: built-in national standard coordinate system one-click adaptation; Supports manual input of custom projection parameters and import of external coordinate parameter files; Supports rapid creation and adaptation of local independent coordinate systems; Supports fully automatic coordinate calculation without POS images; The entire coordinate conversion process meets industry standards for surveying and mapping, with no issues of coordinate offset or distortion.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>6.6 Output Export Interface (Detailed Requirements Description)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface Positioning<\/strong>: Unified output interface for all production deliverables, acceptance documents, and report documents, supporting single file export, batch export of entire projects, format conversion, packaging and archiving, and quality inspection report generation, adapting to full business scenarios such as project delivery, document archiving, surveying and acceptance, and secondary application of results.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Detailed input parameters<\/strong>: target project ID, output type (2D orthophoto image, 3D MESH model, 3D Gaussian OPGS model, laser point cloud, control point report, quality inspection report, runtime), export format, custom export path, batch export scope, compressed packaging label, outcome naming rules.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Output parameter details<\/strong>: standardized achievement files, batch compressed archive packages, achievement integrity verification receipts, automatic generation of acceptance reports, export of runlogs, and file directory index lists.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core functional requirements<\/strong>: 1. Compatible output of multi-format output, supporting general image, model, point cloud, and document formats for the surveying and mapping industry; 2. One-click batch export, supports batch packaging of single categories and full project results, suitable for large-scale project delivery; 3. Intelligent categorized archiving, automatically storing by project, task, and output type, with standardized naming for easy retrieval; 4. Lossless output of results, with no compression, distortion, loss of details, or file corruption during export; 5. Automatically generates acceptance documentation, including quality inspection reports, parameter reports, operation logs, and control point data; 6. Supports export of extremely large files with breakpoints, avoiding interruptions and reruns when exporting large models or large data volumes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Exception handling mechanism<\/strong>: Automatic warnings for insufficient disk space, invalid paths, file usage, export interruptions, etc., automatically saves exported content, supports breakpoint resume export, and ensures the integrity of results.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface purpose<\/strong>: Unified management and control of batch export, format conversion, and packaging archiving of all modeling results, quality inspection documents, and report files in the software, adapting to full-scenario needs for project acceptance, deliverables delivery, and document archiving.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Input parameters<\/strong>: Project ID, output type (orthophoto\/MESH model\/Gaussian model\/point cloud\/control point report), export format, export path, batch export label, compression and packaging parameters.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Output parameters<\/strong>: standardized achievement files, batch packaged compressed packages, output logs, file integrity verification receipts, acceptance report documents.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core business needs<\/strong>: supports single-file, multi-file, and batch export of full project deliverables with one click; Supports output adaptation to mainstream surveying and mapping output formats; Supports automatic naming, categorized archiving, and lossless compression of outputs; Supports automatic generation and export of acceptance reports and quality inspection reports; Ensures large-scale output exports without lag, loss, or 100% integrity.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>6.7 Real-Time Progress Push Interface (Detailed Requirements Description)<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface Positioning<\/strong>: The core interface for real-time front-end and back-end data interaction, based on WebSocket long-term connection communication, enables real-time push of backend computing power status, task progress, modeling stages, and anomaly information to the front-end interface, enabling full-process visualization and seamless automated monitoring, solving traditional software issues such as progress lag, refresh lag, and unclear status.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Input parameter details<\/strong>: client-side unique connection token, subscription project ID, subscription task ID, node monitoring switch, progress refresh frequency, user session identifier.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Output parameter details<\/strong>: real-time task progress percentage, current task running stage (preprocessing\/empty three\/networking\/texture\/Gaussian reconstruction\/result regularization), real-time load on cluster nodes, estimated remaining task duration, real-time operation log, abnormal alarm information, task status change notification, disconnection and reconnection receipt.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core functional requirements<\/strong>: 1. Long-connected real-time push with millisecond-level updates, no need for manual frontend refresh; 2. Simultaneous multitasking and multi-client subscription monitoring, allowing multiple users to view job progress in sync; 3. Detailed display of full-stage progress, precisely distinguishing the progress of each modeling process; 4. Intelligently estimates remaining time and dynamically calculates completion time based on cluster computing power; 5. Real-time abnormal alarms, errors, lag, and instant offline pop-up notifications for nodes; 6. Automatic reconnection when disconnected, status synchronization, long-term batch operations with stable monitoring throughout the process, no data loss, and progress is smooth and disorganized.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Exception handling mechanism<\/strong>: Automatically handles client offline, network fluctuations, connection timeouts, push failures, and other abnormalities, releases invalid connection resources, and automatically synchronizes the latest task status upon reconnection, ensuring monitoring stability.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Interface purpose<\/strong>: Based on WebSocket long-connection real-time push of all backend task running status, hash node load, modeling progress percentage, and anomaly information, enabling real-time updates of the front-end interface without refreshing and ensuring users can visualize and monitor the entire workflow dynamics.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Input parameters<\/strong>: client connection token, project ID, task ID, subscription task type, node monitoring identifier, real-time refresh frequency.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Output parameters<\/strong>: real-time progress percentage, current running stage of the task, real-time load of node CPU\/memory, estimated remaining time of the task, real-time logs, abnormal alarm information, and task status change receipts.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Core business requirements<\/strong>: supports multi-task simultaneous subscriptions and multi-client synchronized monitoring; Millisecond-level progress push, no lag or delay on the interface; Real-time synchronization of task queue, running, pausing, failure, and completion status; Real-time pop-up alerts for abnormal errors and log recording; Supports offline reconnection and automatic reconnection; after reconnection, it automatically synchronizes the latest progress, ensuring stable monitoring of long-term batch operations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Exception handling mechanism<\/strong>: Client automatically disconnects offline subscription, server releases resources; Automatically pushes timeout alerts when tasks freeze; Real-time load alerts are pushed to nodes due to excessive load; If data push fails, it automatically retries three times, ensuring progress data is not lost or messy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>7. Deployment and Operations Design<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>7.1 Deployment Mode<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Standalone deployment<\/strong>: suitable for small projects and single-user operations;<\/li>\n\n\n\n<li><strong>Distributed cluster deployment<\/strong>: multi-node networking to meet large-scale regular production;<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>7.2 Operation and maintenance capabilities<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Automatic logging, exception alarms, and automatic error retrys;<\/li>\n\n\n\n<li>Real-time node status monitoring and visualization of computing power loads;<\/li>\n\n\n\n<li>Automatic data backup, achievement loss prevention, and hierarchical permission control.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>8. Software Function Compliance Summary<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">All functions of this software&nbsp;<strong>fully cover 12 technical parameters listed in the bidding documents, with no negative deviations, complete functionality, compliant performance, and advanced technology<\/strong>. It integrates cluster computing power, AI intelligent processing, 3D Gaussian real-scene reconstruction, multi-hardware adaptation, and fully automated operation capabilities, fully meeting the high-precision, large-scale, and intelligent production needs of the surveying and mapping industry. It can smoothly pass project acceptance and be routinely put into business use.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Developer: Yiwu OOu Import &amp; Export Co., Ltd<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Legal representative: <\/strong>Osman tohsun<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Date of preparation: July 2, 2026<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Documentation version notes Version number Update date Update Details Drafting unit V1.0 2026-07-02 A complete draft of the development documentation includes system architecture, all functional modules, AI algorithms, cluster computing power, database, interfaces, deployment, and hardware adaptation Yiwu OOU Import &amp; Export Co., Ltd 1. Project Overview 1.1 Project Background With the rapid development of&hellip;&nbsp;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[8],"tags":[],"class_list":["post-7511","post","type-post","status-publish","format-standard","hentry","category-new-production"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/silubaba.com.cn\/index.php?rest_route=\/wp\/v2\/posts\/7511","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/silubaba.com.cn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/silubaba.com.cn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/silubaba.com.cn\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/silubaba.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=7511"}],"version-history":[{"count":1,"href":"https:\/\/silubaba.com.cn\/index.php?rest_route=\/wp\/v2\/posts\/7511\/revisions"}],"predecessor-version":[{"id":7512,"href":"https:\/\/silubaba.com.cn\/index.php?rest_route=\/wp\/v2\/posts\/7511\/revisions\/7512"}],"wp:attachment":[{"href":"https:\/\/silubaba.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7511"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/silubaba.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7511"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/silubaba.com.cn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7511"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}