Ship block position real-time monitoring system
Through high-precision grid positioning and intelligent path planning, combined with digital twins and AR navigation, the problem of low efficiency of traditional ship segment positioning has been solved, refined management and resource optimization of the ship manufacturing process have been achieved, and production efficiency and safety have been improved.
Patent Information
- Application Number
- CN202510648559.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-20
- Publication Date
- 2025-10-10
AI Technical Summary
Traditional ship segment positioning relies on manual search, which is inefficient. Barge route planning lacks a global perspective, information is isolated, and there is a lack of intelligent analysis and visualization support, resulting in waste of resources, increased costs and limited production capacity.
It adopts centimeter-level grid positioning and dynamic segmentation-grid binding technology, combines multi-constraint path planning algorithm with LSTM construction period prediction model, integrates 3D digital twin and AR real-time navigation technology, builds a high-precision digital twin site model, realizes segmented second-level positioning, optimizes barge routes and production plans, provides an intuitive human-computer interaction interface, and dynamically responds to emergencies.
It achieves fast and accurate search of segments, optimizes resource allocation and production transportation efficiency, reduces the risk of path conflicts and construction delays, and improves site turnover efficiency and cross-departmental collaboration capabilities.
Smart Images
Figure CN120765147A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of ship section monitoring, in particular to a ship section position real-time monitoring system. BACKGROUND
[0002] Ship section position real-time monitoring is a key link in shipbuilding, and its necessity comes from the comprehensive demand of construction accuracy, efficiency and safety. A ship is assembled by hundreds of large sections, and the weight of a single section can reach several thousand tons, and it needs to be accurately docked in three-dimensional space. If the section position, angle or attitude deviates by millimeters, it may cause welding misplacement, stress concentration and even structural hazards, directly affecting the strength and life of the ship.
[0003] Firstly, traditional section positioning relies on manual on-site search, and the position is recorded by paper boards or simple electronic markers, which is difficult to quickly and accurately locate the target section, resulting in low search efficiency and easy errors. Secondly, the barge transportation path planning lacks a global perspective, and the factory road and site information are not integrated, often causing detours due to improper route selection, increasing time and resource consumption. Thirdly, the tracking management of section duration is insufficient, and it is impossible to monitor the progress in real time and give timely warnings, resulting in section retention in the site, reducing space turnover efficiency. In addition, the information of each link is isolated, and there is no unified data sharing platform, so the production plan, resource allocation and on-site execution are disconnected, making it difficult to achieve collaborative optimization. Finally, the traditional system lacks intelligent analysis and visualization support, and relies on manual experience decision-making, which cannot dynamically respond to sudden conditions such as equipment failure or process changes, further exacerbating management inefficiency and operational risks. These defects together lead to problems such as resource waste, cost increase and limited production capacity in the shipbuilding process. SUMMARY
[0004] (I) Technical problems solved
[0005] In view of the deficiencies of the prior art, the present application provides a ship section position real-time monitoring system, which solves the problems of traditional section positioning relying on manual on-site search and barge transportation path planning lacking a global perspective.
[0006] (II) Technical solutions
[0007] To achieve the above purpose, the present application is implemented by the following technical solutions: a ship section position real-time monitoring system, comprising a data acquisition and modeling module, an intelligent analysis and decision-making module, a collaborative execution and optimization module, and a visualization interaction module;
[0008] The data acquisition and modeling module constructs a high-precision digital twin site model through centimeter-level grid positioning and dynamic section-grid binding technology, realizes section-level positioning, significantly improves site space utilization, completely solves the problems of low efficiency and fuzzy positioning of traditional manual search, and reduces waste of site resources;
[0009] The intelligent analysis and decision module optimizes the transportation path and production plan based on a multi-constraint path planning algorithm and an LSTM construction period prediction model, improves transportation efficiency and resource allocation rationality, effectively reduces path conflict and construction period delay risk, and ensures stable and controllable production rhythm.
[0010] The collaborative execution and optimization module uses dynamic priority scheduling and grid resource adaptive release mechanism to realize intelligent allocation of equipment and site resources, greatly reduces equipment idling and segmented retention problems, improves site turnover efficiency, and releases potential capacity space.
[0011] The visual interaction module integrates 3D digital twin and AR real-time navigation technology to build a man-machine collaborative decision-making interface, provides an intuitive man-machine interface, reduces the operation failure rate, speeds up the abnormal response speed, strengthens the cross-departmental collaboration ability, and simplifies the production management process.
[0012] Preferably, the data acquisition and modeling module includes a site gridding unit and a transportation environment perception unit.
[0013] The site gridding unit cuts the factory area into 10m×10m standard grids by shipyard CAD drawings, high-precision positioning base stations, and segmented unique identifiers, encrypts the boundary to 5m×5m, dynamically binds the segment and the nearest grid by scanning the segmented identifier code, updates the grid occupation state database in real time, and forms a three-dimensional mapping of segment-grid-time stamp.
[0014] Preferably, the transportation environment perception unit collects the crane operation position, segment weight, and transportation track idle state in real time through crane state sensors, track transportation device operation data, and road occupancy monitoring, constructs a dynamic transportation topology map, and labels the restricted area.
[0015] Preferably, the intelligent analysis and decision module includes a path planning unit and a construction period tracking unit.
[0016] The path planning unit uses the start / end grid number, segment size / weight, and crane / track transportation parameters to call the improved A* algorithm to calculate the initial shortest transportation path, superimposes the segment physical constraints and device load capacity, generates a multimodal transportation scheme, and selects the path with the optimal comprehensive efficiency.
[0017] Preferably, the construction period tracking unit uses the segment planned construction period, work station scan code progress, and device resource occupancy rate to adopt an LSTM time series prediction model to dynamically calculate the remaining construction period and trigger a three-level early warning mechanism.
[0018] Red (overdue > 3 days): forced insertion into the primary production queue to seize crane resources;
[0019] Yellow (Remaining Duration < 20%): Automatically assign standby welding machine team;
[0020] Green: Baseline rolling update.
[0021] Preferably, the collaborative execution and optimization module includes a transfer scheduling unit and a site resource optimization unit.
[0022] The transfer scheduling unit dynamically adjusts the transportation priority according to the duration color label using the optimal path instruction, crane / rail device real-time state, and duration urgency. When the transportation equipment deviates from the planned path by >1m, local re-planning is triggered. In the multi-crane collaborative scenario, a conflict detection algorithm is used to avoid equipment collision.
[0023] Preferably, the site resource optimization unit uses grid occupancy, segmented duration deviation, and production plan changes. When the grid occupancy is >85%, it automatically releases the standby buffer grid, initiates forced migration instructions for stranded overdue segments, and associates crane scheduling to generate monthly turnover rate reports and recommend inefficient grid modification schemes.
[0024] Preferably, the visual interaction module includes a digital twin cockpit and a mobile interaction terminal.
[0025] The data twin cockpit displays the real-time state of the entire factory area in 3D through the grid map, path heat map, and duration warning data, supports hierarchical focusing, and can penetrate to view historical trajectories by clicking on segments. It also has a sand table deduction function to simulate the impact of site expansion or process changes.
[0026] The mobile interaction terminal uses AR camera pictures, scan code data, and voice instructions to automatically superimpose AR navigation arrows to guide to the target grid after scanning the segment label. When reporting abnormalities on site, it automatically freezes the related area path planning.
[0027] (Three) Beneficial effects
[0028] The present application provides a ship segment position real-time monitoring system. It has the following beneficial effects:
[0029] 1. This invention uses high-precision grid positioning and dynamic binding technology to achieve rapid and accurate search of ship sections, solving the problem of low efficiency of traditional manual positioning. It optimizes barge path planning and construction period prediction based on intelligent algorithms, significantly improving transportation efficiency and the rationality of resource allocation, reducing the risk of path conflicts and construction period delays, and effectively reducing equipment idleness and site detention through dynamic resource scheduling and adaptive optimization mechanisms, thereby improving resource utilization and turnover efficiency. Combining digital twins with AR interaction technology, it provides an intuitive real-time visualization interface, reduces operational complexity and enhances cross-departmental collaboration capabilities. From spatial mapping, intelligent decision-making to execution optimization, the closed-loop management system promotes the transformation of shipbuilding from experience-driven to data-intelligent-driven, realizes refined management and control of production processes and efficiency leaps, and provides the industry with replicable digital solutions. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] Figure 1 This is an overall flow chart of a real-time monitoring system for ship segment positions proposed by the present invention. DETAILED DESCRIPTION
[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0032] Example:
[0033] like Figure 1 As shown, an embodiment of the present invention provides a real-time monitoring system for ship segment positions, including a data acquisition and modeling module, an intelligent analysis and decision module, a collaborative execution and optimization module, and a visual interaction module;
[0034] The data acquisition and modeling module uses centimeter-level grid positioning and dynamic segmentation-grid binding technology to build a high-precision digital twin site model, achieving segmented positioning within seconds, significantly improving site space utilization, and completely solving the problems of low efficiency and ambiguity in traditional manual search, reducing site resource waste. The segmented search response time is less than 1 second, the positioning accuracy is ≤10cm, and the site space utilization rate is increased by 40%.
[0035] The intelligent analysis and decision-making module, based on a multi-constrained path planning algorithm and an LSTM duration prediction model, optimizes barge routes and production plans, improves transportation efficiency and rationalizes resource allocation, effectively reduces the risk of route conflicts and construction delays, and ensures a stable and controllable production rhythm. Barge route efficiency has increased by 25%, construction duration prediction accuracy has exceeded 90%, and resource waste has been reduced by 35%.
[0036] The cooperative execution and optimization module adopts dynamic priority scheduling and grid resource adaptive release mechanism to realize intelligent allocation of equipment and site resources, greatly reduce equipment idle and segmented retention problems, improve site turnover efficiency, release potential capacity space, reduce crane idle rate by 50%, reduce segmented retention rate by 60%, and increase turnover rate to 95%;
[0037] The visual interaction module integrates 3D digital twin and AR real-time navigation technology to build a man-machine collaborative decision-making interface, provides an intuitive man-machine interface, reduces the operation error rate, speeds up the abnormal response speed, strengthens the cross-department collaboration ability, simplifies the production management process, reduces human operation errors by 80%, the abnormal response time is less than 30 seconds, and the cross-department collaboration efficiency is doubled.
[0038] System operation logic: data closed loop:
[0039] The acquisition layer captures the segmented position in real time→ the modeling module updates the grid relationship→ the analysis module generates decisions→ the execution module drives the crane / track device→ the interaction layer feeds back the execution results→ the acquisition layer starts the next cycle of monitoring;
[0040] Intelligent upgrade:
[0041] Historical path data training algorithm parameters→ project duration prediction model iterative optimization→ grid division rules adaptive adjustment (such as dynamically scaling grid density according to segmented size);
[0042] Emergency response:
[0043] In the event of sudden equipment failure, automatically mark the affected segment as red→ trigger cross-department collaborative meeting→ reassign resources and update the plant plan baseline.
[0044] The data acquisition and modeling module includes a site gridding unit and a transfer environment perception unit;
[0045] The site gridding unit cuts the plant area into 10m×10m standard grids by shipyard CAD drawings, high-precision positioning base stations, and unique segment identifiers, and encrypts the boundary to 5m×5m. By scanning the segment identifier code, the segment is dynamically bound to the nearest grid, and the grid occupancy state database is updated in real time to form a three-dimensional mapping of segment-grid-time stamp.
[0046] The transfer environment perception unit collects crane operating position, segment weight, and transportation track idle state in real time through crane state sensors, track transportation device operation data, and road occupancy monitoring, constructs a dynamic transfer topology map, and marks the restricted area.
[0047] The intelligent analysis and decision module includes a path planning unit and a project duration tracking unit;
[0048] The path planning unit utilizes the start / endpoint grid number, the segment size / weight, the crane / rail transportation parameters, calls the improved A* algorithm, calculates the initial shortest transportation path, superimposes the segment physical constraints, and the equipment load capacity; generates a multimodal transportation scheme, and selects the path with the optimal comprehensive efficiency.
[0049] The construction period tracking unit utilizes the segment plan construction period, the station scan code progress, and the equipment resource occupancy rate, adopts the LSTM time series prediction model, dynamically calculates the remaining construction period, and triggers a three-level early warning mechanism.
[0050] Red (overdue > 3 days): forced insertion into the priority production queue, and occupation of crane resources;
[0051] Yellow (remaining construction period < 20%): automatically assign standby welder teams;
[0052] Green: baseline rolling update.
[0053] The collaborative execution and optimization module includes a transfer scheduling unit and a site resource optimization unit.
[0054] The transfer scheduling unit utilizes the optimal path instructions, the real-time state of the crane / rail device, and the construction period urgency, dynamically adjusts the transportation priority according to the construction period color label, triggers local re-planning when the transportation device deviates from the planned path by > 1m, and in the multi-crane collaborative scenario, uses a conflict detection algorithm to avoid device collision.
[0055] The site resource optimization unit utilizes the grid occupancy rate, the segment construction period deviation, and the production plan changes, automatically releases the standby buffer grid when the grid occupancy rate > 85%, initiates a forced migration instruction for the segments with overdue periods, and associates the crane scheduling, generates a monthly turnover rate report, and recommends a low-efficiency grid modification scheme.
[0056] The visualization interaction module includes a digital twin cockpit and a mobile interaction terminal.
[0057] The data twin cockpit displays the real-time state of the entire factory area in 3D through the grid map, the path heat map, and the construction period warning data, supports hierarchical focusing, and can penetrate to view historical trajectories by clicking on segments, embeds a sand table deduction function to simulate the impact of site expansion or process changes;
[0058] The mobile interaction terminal automatically superimposes an AR navigation arrow to guide to the target grid after scanning the segment label, and automatically freezes the relevant area path planning when reporting abnormalities on site.
[0059] In summary: through the "perception-decision-execution-optimization" four-step cycle, we achieve:
[0060] Spatial efficiency: grid utilization rate increased from 60% to 92%;
[0061] Time controllable: The time limit compliance rate is increased to more than 95%;
[0062] Resource coordination: The crane idle rate is reduced by 40%, and the rail transportation conflict rate is less than or equal to 0.5%;
[0063] Safety enhancement: The segmented positioning accuracy is up to centimeter level, and the AR navigation reduces the human operation error rate by 90%. The application focuses on the precise positioning and intelligent scheduling of ship segments in the factory area. By eliminating vehicle-related redundant design, the professional adaptability of the shipbuilding production scene is further strengthened, providing core support for the digital upgrading of the shipbuilding industry.
[0064] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.
Claims
1. A real-time monitoring system for ship segment positions, characterized in that: It includes data acquisition and modeling module, intelligent analysis and decision-making module, collaborative execution and optimization module, and visual interaction module; The data acquisition and modeling module uses centimeter-level grid positioning and dynamic segmentation-grid binding technology to build a high-precision digital twin site model, achieving segmented second-level positioning, significantly improving site space utilization, and completely solving the problems of low efficiency and fuzzy positioning of traditional manual search, thereby reducing waste of site resources. The intelligent analysis and decision-making module, based on a multi-constraint path planning algorithm and an LSTM construction period prediction model, optimizes barge routes and production plans, improves transportation efficiency and rational resource allocation, effectively reduces the risk of route conflicts and construction period delays, and ensures a stable and controllable production rhythm. The collaborative execution and optimization module adopts dynamic priority scheduling and grid resource adaptive release mechanism to realize intelligent allocation of equipment and site resources, significantly reducing equipment idleness and segment retention problems, improving site turnover efficiency, and releasing potential production capacity space; The visual interaction module integrates 3D digital twins and AR real-time navigation technology to build a human-computer collaborative decision-making interface, provide an intuitive human-computer interaction interface, reduce operational error rates, accelerate abnormal response speed, strengthen cross-departmental collaboration capabilities, and simplify production management processes.
2. A real-time monitoring system for ship segment positions according to claim 1, characterized in that: The data acquisition and modeling module includes a site gridding unit and a barge environment perception unit; The site gridding unit uses shipyard CAD drawings, high-precision positioning base stations, and segment unique identifiers to cut the factory area into a 10m×10m standard grid, and encrypt the boundaries to 5m×5m. By scanning the segment identification code, the segment is dynamically bound to the nearest grid, and the grid occupancy status database is updated in real time to form a three-dimensional mapping of segment-grid-timestamp.
3. A real-time monitoring system for ship segment positions according to claim 2, characterized in that: The barge environment perception unit collects crane operating position, segment weight, and transport track idle status in real time through crane status sensors, rail transport device operation data, and road occupancy monitoring, constructs a dynamic barge topology map, and marks restricted areas.
4. A real-time monitoring system for ship segment positions according to claim 1, characterized in that: The intelligent analysis and decision-making module includes a path planning unit and a construction period tracking unit; The path planning unit uses the start / end point grid numbers, segment size / weight, crane / track transportation parameters, calls the improved A* algorithm, calculates the initial shortest transportation path, superimposes the segment physical constraints and equipment load capacity, generates a multimodal transportation plan, and selects the path with the best overall efficiency.
5. A real-time monitoring system for ship segment positions according to claim 4, characterized in that: The construction period tracking unit uses the segmented planned construction period, workstation scanning progress, and equipment resource occupancy rate, and adopts an LSTM time series prediction model to dynamically calculate the remaining construction period and trigger a three-level early warning mechanism: Red (overdue > 3 days): forced to insert into the priority production queue and seize crane resources; Yellow (remaining construction period < 20%): automatic allocation of standby welding machine team; Green: Baseline rolling update.
6. A real-time monitoring system for ship segment positions according to claim 1, characterized in that: The collaborative execution and optimization module includes a barge scheduling unit and a site resource optimization unit The barge dispatching unit uses the optimal path instructions, the real-time status of the crane / track device, and the urgency of the construction period to dynamically adjust the transportation priority according to the construction period color label. When the transportation equipment deviates from the planned path by more than 1m, local replanning is triggered. In the multi-crane collaborative scenario, a conflict detection algorithm is used to avoid equipment collisions.
7. A real-time monitoring system for ship segment positions according to claim 6, characterized in that: The site resource optimization unit uses grid occupancy rate, segment duration deviation, and production plan changes. When the grid occupancy rate is greater than 85%, it automatically releases the spare buffer grid, initiates a forced migration instruction for the stranded segment that has exceeded the deadline, and associates it with crane scheduling to generate a monthly turnover rate report and recommend inefficient grid transformation plans.
8. A real-time monitoring system for ship segment positions according to claim 1, characterized in that: The visual interaction module includes a digital twin cockpit and a mobile interactive terminal; The data twin cockpit uses grid maps, path heat maps, and construction period warning data to display the real-time status of the entire plant in 3D. It supports layered focus, and users can click on each segment to view the historical trajectory. It also embeds a sandbox simulation function to simulate the impact of site expansion or process changes. The mobile interactive terminal uses AR camera images, scanned code data, and voice commands to automatically superimpose AR navigation arrows to guide to the target grid after scanning code segmentation labels. When an abnormality is reported on site, the path planning of the relevant area is automatically frozen.