Intelligent inventory management method based on inspection robot

Abstract

The invention discloses an intelligent inventory management method based on an inspection robot, and belongs to the technical field of automatic warehouse management. In order to solve the problems that traditional manual checking is low in efficiency and prone to making mistakes, an existing fixed sensor or RFID system is limited in coverage and poor in flexibility, and a solution for deep fusion of an inspection robot, AI visual recognition and other technologies is lacked, the technical scheme is characterized in that the inspection robot conducts autonomous navigation inspection through the SLAM technology, multiple sensors are used for collecting multi-source data; and after fusion processing, CNN model identification and RFID dual verification, the system is compared with an ERP / WMS system to update the inventory, abnormal early warning and intelligent decision support are realized, and the robot can continuously work in all weathers. The method is used for warehouse inventory management, the intelligent level and the operation efficiency of inventory management can be improved, the labor cost is reduced, and the warehouse safety and flexibility are enhanced.

Description

Technical Field

[0001] This invention relates to the field of automated warehouse management technology, and in particular to an intelligent inventory management method that combines artificial intelligence, the Internet of Things (IoT) and robotics, specifically an intelligent inventory management method based on inspection robots. Background Technology

[0002] With the development of e-commerce and intelligent manufacturing, enterprises have placed higher demands on the efficiency, accuracy, and real-time nature of warehouse management. Traditional manual inventory methods suffer from problems such as high labor intensity, susceptibility to errors, and long cycles; while existing fixed sensor or RFID systems, although improving the degree of automation, still have shortcomings such as limited coverage, high deployment costs, and poor flexibility.

[0003] In recent years, inspection robots have been widely used in fields such as power and security. Introducing them into warehouse management can enable dynamic perception and autonomous inspection of the warehouse environment. However, current technologies lack a complete solution that deeply integrates inspection robots with AI visual recognition, data fusion analysis, and inventory decision-making systems, making it difficult to achieve truly "unmanned" and "intelligent" inventory management.

[0004] Therefore, there is an urgent need for a new inventory management method that can automatically identify, update in real time, analyze intelligently, and provide proactive early warnings. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent inventory management method based on inspection robots. By integrating a mobile robot platform, a multimodal perception system and artificial intelligence algorithms, it can achieve automatic identification, accurate positioning, dynamic inventory counting and anomaly warning of items in the warehouse, thereby improving the intelligence level and operational efficiency of inventory management. Detailed Implementation

[0006] To achieve the above objectives, the present invention provides an intelligent inventory management method based on an inspection robot, comprising the following steps: Step 1: Path Planning and Autonomous Inspection • Inspection robots can navigate autonomously in the warehouse environment using SLAM (Simultaneous Localization and Mapping) technology, based on preset tasks (such as daily routine inspections or spot checks of specific areas) or dynamic instructions. • The robot moves along the optimal path to ensure coverage of all critical storage areas.

[0007] Step 2: Multi-source data acquisition • During the inspection process, the robot collects data using various onboard sensors: • Visual camera: Captures images of the shelves for subsequent image recognition; • RFID reader: Reads information from electronic tags attached to goods or pallets; • LiDAR / depth camera: Acquires 3D spatial information to assist in localization and obstacle avoidance; • Temperature and humidity sensors: monitor warehouse environmental parameters; • Infrared sensor: Detects whether people or animals have illegally entered the premises.

[0008] Step 3: Data Fusion and Item Recognition • Transmit the collected multi-source data to the local edge computing unit or cloud server; • Use fusion algorithms (such as Kalman filtering and deep learning fusion networks) to fuse data from different sensors; • Use a trained convolutional neural network (CNN) model to perform object detection and classification on images, and identify SKU (stock keeping unit) types; • Combine RFID information for dual verification to improve identification accuracy.

[0009] Step 4: Inventory Status Analysis and Update • Compare the identification results with the inventory records in the enterprise's ERP / WMS system; • Automatically update the actual inventory quantity and location information in the system; • Calculate inventory discrepancies and generate inventory count reports.

[0010] Step 5: Anomaly Detection and Intelligent Early Warning • The system triggers an early warning mechanism when it detects the following situations: • The actual inventory quantity does not match the system record (profit and loss difference); • Goods are placed in the wrong location (misplaced); • Unauthorized movement of high-value items; • Environmental parameters exceed safety thresholds (e.g., excessively high temperature); • The robot's inspection path is blocked or the equipment is malfunctioning; • Warning information is pushed to managers via SMS, email, or internal corporate communication tools.

[0011] Step Six: Intelligent Decision Support • Based on historical data and current inventory status, build predictive models (such as LSTM neural networks) to predict future demand trends; • Provide suggestions for replenishment, inventory allocation, or layout optimization; • Supports integration with ERP systems to automatically generate purchase orders or transfer tasks; • The inspection robot has an automatic charging function. When the battery level is lower than a set threshold, it can return to the charging station to charge, enabling continuous operation 24 / 7. • The system supports a remote monitoring interface, allowing managers to view robot location, inspection progress, inventory status, and alarm information in real time via the web or mobile device.

[0012] Compared with the prior art, the present invention has the following advantages: • High-precision recognition: Employing multi-sensor fusion and deep learning technology, it significantly improves the accuracy of object recognition and reduces the false recognition rate and the missed recognition rate; • Fully automated operation: Robots can perform autonomous inspections around the clock, reducing human intervention and lowering labor costs; • Real-time and dynamic: Supports high-frequency, dynamic inventory counts, enabling near real-time updates of inventory status; • Proactive early warning capability: It can promptly detect inventory anomalies, environmental risks, and equipment failures, thereby improving warehouse security; • Data-driven decision-making: Combining AI predictive models, it provides intelligent replenishment and optimization suggestions to optimize inventory structure; • Flexible deployment: Mobile robots can flexibly adjust their inspection paths according to the warehouse layout, making them highly adaptable and scalable.

Claims

1. An intelligent inventory management method based on inspection robots, characterized in that... Includes the following steps: The inspection robot moves autonomously within the warehouse according to a predetermined path or a dynamically planned path. The inspection robot uses sensors installed on it to scan and identify goods in the warehouse. The collected data is transmitted to the central processing unit for analysis and to update inventory information; The system automatically adjusts inventory layout or issues replenishment orders based on the analysis results.

2. The method according to claim 1, characterized in that, The sensors include, but are not limited to, RFID readers, barcode scanners, laser rangefinders, and cameras, used to acquire information such as the location, quantity, and status of the goods.

3. The method according to claim 1, characterized in that, The central processing unit uses image recognition technology and machine learning algorithms to analyze the visual data captured by the camera in order to determine the specific location and status changes of the goods.

4. The method according to claim 1, characterized in that, It also includes the following steps: when an anomaly is detected (such as lost or damaged goods), the system can automatically generate a report and send a notification to the management personnel via the network.

5. The method according to claim 1, characterized in that, The method further includes a forecasting module that predicts future demand trends based on historical sales data and current inventory levels, and optimizes inventory allocation strategies accordingly.

6. The method according to claim 1, characterized in that, The inspection robot has a self-charging function and can automatically return to the charging station to recharge when the battery level is lower than a preset threshold.

7. An intelligent inventory management system based on inspection robots, comprising: At least one inspection robot is used to perform the method according to any one of claims 1-6; The central control system is responsible for receiving and processing data from the inspection robots. Network communication devices ensure data exchange between the inspection robot and the central control system; A database is used to store all relevant data, including but not limited to goods information, inventory records, and operation logs.

8. The system according to claim 7, characterized in that, The central control system can also be integrated with other enterprise resource planning (ERP) systems to share data and improve overall operational efficiency.

9. The system according to claim 7, characterized in that, The inspection robot can be wheeled, tracked, or other types, depending on the requirements of the warehouse environment.