Physical asset space management method based on three-dimensional point cloud coordinate mapping

Abstract

The invention discloses a physical asset space management method based on three-dimensional point cloud coordinate mapping, and relates to the field of three-dimensional coordinate reconstruction and warehouse management. The method comprises the following steps: performing three-dimensional point cloud modeling on a warehouse space, and constructing a unified coordinate system; collecting three-dimensional point cloud data of goods, generating a unique identifier and binding the unique identifier with a digital model; the digital model of the goods is anchored into a storage coordinate system through a point cloud registration algorithm, and the attitude angle and the spatial position of the goods are calculated; analyzing the geometrical relationship between the goods and the storage positions, and identifying space states such as border crossing, stacking and posture abnormity; and calculating a space utilization rate based on the cargo volume and the storage location volume, and outputting an optimization suggestion. According to the invention, ontology-level coupling management of the goods and the storage space is realized, space deduction type account checking and checking and anomaly detection are supported, and the method is suitable for complex storage scenes such as special-shaped objects and high-density stacking.

Description

Technical Field

[0001] The present invention relates to the fields of three-dimensional coordinate reconstruction and warehouse management, and in particular to a physical asset space management method based on three-dimensional point cloud coordinate mapping, which can be applied to warehouse management to achieve high-precision dynamic physical anchoring. Background Art

[0002] Traditional warehousing systems rely on barcodes, RFID tags, or manual labeling to manage goods, recording only the name, quantity, and storage location number. These systems suffer from data abstraction, imprecise location, and lack of visibility, making precise management particularly challenging when handling large, irregularly shaped, and stacked objects.

[0003] 3D point cloud technology, with its high-precision and highly accurate spatial modeling capabilities, has been widely used in fields such as autonomous driving and industrial inspection. Despite continuous advancements in 3D scanning hardware and point cloud algorithms, methods and systems for integrated spatial coupling management of physical assets and storage environments remain lacking.

[0004] Therefore, there is an urgent need for a new method system that can model warehouse structures and physical assets into point clouds, and realize spatial coordinate anchoring, dynamic posture tracking and space utilization analysis, so as to achieve true "ontology-level" asset space management. Summary of the Invention

[0005] The present invention aims to provide a physical asset space management method based on three-dimensional point cloud coordinate mapping. By constructing a three-dimensional point cloud model of storage space coordinates and goods, the dynamic anchoring, posture calculation and volume-level management of physical assets in real space can be realized, thereby realizing high-density, intelligent warehouse scheduling and monitoring.

[0006] The method of the present invention comprises the steps of:

[0007] Step 1: 3D coordinate modeling of warehouse space 1. Use 3D laser radar, structured light scanner and other equipment to perform 3D scanning of the internal structure of the warehouse to generate complete point cloud data of the storage space; 2. Set anchor points in the physical environment (such as QR code targets, reflective balls, etc.) and establish a global 3D coordinate system through the SLAM coordinate registration algorithm; 3. Encode and identify objects such as shelves, storage spaces, walls, and aisles in the coordinate system to achieve three-dimensional coordinate modeling of the storage space.

[0008] Step 2: 3D coordinate modeling of physical goods 1. Use 3D scanning equipment to model each piece of cargo, obtain its point cloud data, and obtain its 3D morphological parameters such as length, width, height, volume, and posture; 2. Generate a unique digital identity (such as UID) for each piece of cargo and bind it to its point cloud data; 3. Store in the point cloud database as the digital basis for subsequent spatial anchoring and identification.

[0009] Step 3: Cargo coordinate anchoring (core step)

[0010] This step aims to accurately anchor each modeled 3D point cloud of physical goods in the warehouse's unified coordinate system and maintain real-time updates. Its key features are the "point cloud to point cloud" spatial matching mechanism and dynamic posture calculation, specifically including:

[0011] 1. Point cloud alignment and matching: Assume that the cargo point cloud is , the warehouse reference point cloud is , solve the rigid body transformation matrix through the iterative closest point algorithm:

[0012] Among them, R is the rotation matrix; t is the translation matrix, and T is the final registration result.

[0013] Pose angle calculation: Extract the minimum bounding box of the cargo and obtain its local principal axis vector group:

[0014] Then, it is combined with the warehouse global coordinate system unit vector

[0015] Calculate the included angle to get the attitude angle of each axis:

[0016] Among them, vectors X, Y, and Z represent the three main axis vectors of the cargo bounding box in the local coordinates, and vectors i, j, and k represent the global reference axis unit vectors of the warehouse coordinates.

[0017] 3. Anchor record generation: Generate a "cargo space anchor record" based on the matching results, recording coordinates, attitude angles, timestamps, and other content.

[0018] Step 4: Spatial Coupling and Volume Management (Core Step)

[0019] This step aims to establish the "logical coupling" and "geometric analysis" relationship between the goods and the warehouse space structure, which is the key to achieving entity-level asset management and spatial anomaly warning.

[0020] 1. Calculation of space occupied volume

[0021] Volume of cargo bounding box: Corresponding storage volume: Space utilization: If U is less than the threshold, the system will trigger a "space waste prompt".

[0022] 2. Overlap and cross-border detection

[0023] Cross-boundary judgment: If there is a non-intersection area between the cargo OBB boundary and the storage location boundary:

[0024] Stacking conflict detection: Calculate the intersection volume (or point cloud density intersection area): If R is greater than the threshold, the system prompts "Stack Violation". BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to assist in understanding the present invention and are not intended to limit the scope of protection.

[0026] Figure 1 This is a structural diagram of the asset management system based on three-dimensional point cloud coordinate mapping described in the present invention, which mainly includes a point cloud acquisition module, a registration and posture analysis module, a spatial coupling judgment module, and a data output and inventory module. Each module cooperates in turn to realize the spatial anchoring and management of physical assets.

[0027] Figure 2 This is a flowchart of cargo point cloud anchoring and attitude angle calculation, showing how this system completes the spatial positioning and attitude state expression of cargo in the warehouse coordinate system through point cloud registration, OBB extraction, and attitude angle calculation.

[0028] Figure 3 This is a flowchart for the system's automatic inventory and book data comparison, demonstrating how the system detects discrepancies between accounts and actuals and generates inventory reports through point cloud anchoring and database comparison without human intervention. Specific embodiments

[0029] In order to make the purpose, technical solution and beneficial effects of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. The present invention is not limited to the following specific embodiments, and any equivalent replacement or improvement under the spirit and principle of the present invention should be included in the scope of protection of the present invention.

[0030] Example 1: Automatic Inventory Counting Based on 3D Point Cloud Anchoring

[0031] In a mechanical parts warehouse, the system of the present invention was deployed. Before entering the warehouse, all goods were scanned using a structured light scanner to generate 3D point cloud data and associated with a unique UID. After the goods were located in the warehouse, the system used a point cloud registration algorithm to anchor them to the warehouse coordinate system.

[0032] Every night the system automatically scans the entire warehouse space and performs coordinate comparisons: Identify new, missing, or misplaced goods; Calculate the actual capacity of the current storage space; Compare the book inventory data and automatically generate a list of "discrepancies between book and actual inventory"; Realize unmanned inventory and traceable records.

[0033] This application realizes the management transition from "quantity recording" to "ontology perception".

[0034] Example 2: Space Boundary and Stacking Anomaly Detection

[0035] In a parts warehouse with densely stacked shelves, some goods are offset or stacked crookedly when actually placed, which can easily lead to the risk of slipping.

[0036] After anchoring the cargo coordinates, the system automatically extracts the OBB bounding box of each cargo and performs spatial overlap detection with the target storage location boundary. The judgment logic is as follows: If any dimension of the cargo bounding box exceeds the storage location boundary, the system determines it as "out of bounds"; If the point cloud overlap rate R between two goods is greater than 0.25, the system will mark it as "stacking violation".

[0037] The detection results are used for on-site sound and light alarms, or uploaded to the system to generate abnormal distribution maps to assist warehouse managers in correcting cargo positions.

[0038] Example 3: Volume Utilization Analysis and Automatic Shelving Suggestions

[0039] In a large e-commerce warehouse, the types of goods are complex and the sizes vary greatly, resulting in waste of space.

[0040] After completing cargo anchoring, the system automatically counts the following for each storage location: Actual occupied volume V actual ; Theoretical volume V storage ; Utilization U = V storage / V actual。

[0041] If U < 0.5, the system will generate optimized shelf arrangement suggestions based on the size of the goods and the frequency of warehousing: Large cargo is concentrated at the lower level; High-frequency cargo is concentrated near entrances and exits; Combination storage of goods of similar size.

[0042] This function effectively improves the overall space utilization of the warehouse by approximately 20%.

[0043] Example 4: Abnormal posture detection and anti-tilt warning

[0044] In a high-value equipment storage environment, some equipment tilted slightly during transportation, posing a risk of collapse.

[0045] The system of the present invention calculates the three-axis angle of the cargo through the attitude angle formula:

[0046] If the angle θ of any axis is greater than 10∘, an early warning record will be generated. The early warning information includes: Cargo UID; Posture deviation angle; The current storage layer number.

[0047] The system combines multiple scans for stability analysis to assist in determining tilt trends and enable early intervention.

[0048] Example 5: Automatic calculation of space capacity and intelligent storage strategy

[0049] In a smart warehouse for a clothing e-commerce company, the system automatically calculates the maximum quantity that can be put into storage based on the current available storage space volume and the standard size of the goods in stock. The calculation formula is:

[0050] The system compares this quantity with the order quantity: If there is insufficient space, it will automatically prompt you to postpone storage or split the warehouse; It can provide a "recommended warehousing route" to guide forklift drivers to enter the warehouse to the optimal storage location first.

[0051] This function greatly reduces manual trial and error in warehousing, improving the smoothness and accuracy of the process.

Claims

1. A physical asset space management method based on three-dimensional point cloud coordinate mapping, characterized in that: It includes the following steps: (1) Warehouse space modeling: Scanning the warehouse structure using, including but not limited to, 3D lidar or structured light devices to generate warehouse point cloud data; Establishing a global three-dimensional coordinate system by setting anchor points; Coordinate encoding of spatial structures such as storage locations and shelves; (2) Goods modeling and binding: Obtaining the three-dimensional point cloud data of each piece of goods, extracting parameters such as its size, attitude, volume, etc., assigning it a unique identification code UID, and storing the data in the point cloud database; (3) Goods coordinate anchoring: Registering and matching the goods point cloud model with the warehouse point cloud to obtain a rigid body transformation matrix, determining the position and attitude of the goods in the warehouse coordinate system, extracting bounding box information, and generating an anchoring record; (4) Attitude angle calculation: Calculating the attitude angles θx, θy, θz of the goods according to the angles between the main axis vectors of the goods bounding box and the warehouse coordinate axes, which is used to identify tilting or abnormal attitudes; (5) Spatial coupling analysis: Calculating the spatial relationship between the spatial boundary of the anchored goods and the target storage location boundary to determine whether there are situations such as overstepping, overlapping, or abnormal attitudes; (6) Volume utilization rate calculation: Based on the storage location volume Vstorage and the actual occupied volume Vactual of the goods, calculating the space utilization rate U = Vstorage / Vactual, and outputting optimization suggestions or abnormal alarms accordingly.

2. The method according to claim 1, characterized in that In step (3), the registration and matching is realized by using the Iterative Closest Point (ICP) or Feature Histogram of Aligned Normal (FPFH) algorithm to align the point clouds.

3. The method according to claim 1, characterized in that The attitude angle calculation in step (4) adopts the following formula:

4. Where the vectors X, Y, Z are the main axis vectors of the bounding box respectively, and the vectors i, j, k are the unit vectors of the warehouse coordinate axes respectively.

5. The method according to claim 1, wherein The judgment of the spatial overlap rate adopts the following ratio: When R is greater than the threshold, the system determines it as a stacking violation.

6. The method according to claim 1, wherein When the space utilization rate U < Umin, the system generates a shelving optimization suggestion.

7. The method according to claim 1, characterized in that The anchoring record at least includes: goods number UID, central coordinates, attitude angle, bounding box size, anchoring timestamp, and source device number.

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