Intelligent warehouse management system based on visual identification and operation method

Abstract

The invention discloses an intelligent warehouse management system based on visual identification and an operation method, and relates to the technical field of intelligent warehouse management, and the system comprises a tool information management module, a tool scheduling execution module, a visual identification module, a tool access checking module and a data analysis module. The types and the number of tools are verified through visual identification and dynamic weighing, efficient storing and taking operation is achieved in combination with automatic carrying equipment and a grabbing mechanical arm, and the storage space utilization rate is optimized. According to the method, the warehouse management efficiency can be remarkably improved, the inventory data accuracy is enhanced, and a full-process automatic solution is provided for tool management in a hydraulic turbine set maintenance scene.

Description

Technical Field

[0001] This invention belongs to the field of intelligent warehousing, and specifically relates to an intelligent warehouse management system and its operation method based on visual recognition. Background Technology

[0002] Intelligent warehouse management technology plays a crucial role in industrial production and equipment maintenance, especially for the maintenance of large equipment such as hydroelectric turbines. This is because the tools are diverse and complex, requiring extremely high efficiency in storage and retrieval, as well as high inventory accuracy. Traditional warehouse management methods rely on manual recording and operation, which is not only inefficient but also prone to human error, leading to misplacement, omissions, or quantity discrepancies, severely impacting the progress and safety of maintenance work.

[0003] To address the aforementioned issues, the applicant's previously disclosed patent (CN118529408A) proposes an intelligent toolbox storage cabinet and its operating method. Through a main frame, a palletizing device corridor, and a bidirectional multi-layer stacker truck, it achieves automated handling and stacking of tool cabinets and toolboxes, effectively reducing manual workload and improving operational safety and efficiency. While this technology has initially automated the physical operation of tool storage through the automation of its mechanical structure, there is still room for optimization in refined aspects such as tool type identification, quantity verification, and dynamic management. For example, it primarily relies on preset mechanical paths for handling, lacking the ability to intelligently identify the characteristics of the tools themselves, and cannot automatically verify whether the types and quantities of stored tools match. Furthermore, the allocation of tool storage locations is not optimized based on dynamic data such as tool usage frequency, volume, and weight, making it difficult to further improve warehouse space utilization and retrieval efficiency.

[0004] Based on this, in order to meet the demand for automation and precision in the entire process of tool management in scenarios such as turbine maintenance, there is an urgent need for an intelligent warehouse management system that integrates visual recognition and dynamic perception technologies. On the basis of existing automated handling, it can further realize the automatic identification of tool types, accurate verification of quantities, and intelligent optimization of storage strategies, thereby building a more efficient and reliable intelligent warehouse management system. Summary of the Invention

[0005] The technical problem this invention aims to solve is to provide a vision-based intelligent warehouse management system and its operation method, which automates the entire process of tools from entry and storage to exit, reducing manual intervention. Especially in scenarios involving a wide variety of tools and complex operations, such as turbine maintenance, it can significantly reduce manual labor intensity, avoid human error, and improve management efficiency.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A vision-based intelligent warehouse management system includes a tool information management module, a tool scheduling and execution module, a vision recognition module, a tool storage and retrieval verification module, and a data analysis module. The tool information management module is used to record basic tool information and generate tool entry and exit instructions; The tool scheduling and execution module is used to complete the storage and retrieval operations of tools according to the tool entry and exit instructions; The visual recognition module is used to identify the type of tool during tool access. The tool access verification module is used to verify the quantity and type of tools during the tool access process; The data analysis module is used to perform statistical analysis on the historical data of tools entering and leaving the database. Preferably, the tool information management module includes a tool attribute input unit, a storage location allocation unit, an image feature extraction unit, a weight information processing unit, and a task scheduling unit; The tool attribute input unit is used to record the name, model, and specifications of the tool and store this information in the database; The image feature extraction unit acquires the appearance image of the tool using a 3D camera and extracts shape and color features as a basis for recognition. The weight information processing unit measures the weight of the tool using a high-precision electronic scale and stores the weight data in a database. The storage location allocation unit is used to calculate the optimal storage location based on the tool's size, weight, and usage frequency, and then associate and store this location information with the tool information. The task scheduling unit is used to receive tool entry / exit instructions from the user and send specific operation tasks to the tool scheduling and execution module.

[0007] Preferably, the tool scheduling and execution module includes an automated handling device, a path planning unit, and a gripping robotic arm; The automated handling equipment uses AGVs to move along a preset path within the storage area and transport tools to a designated location; The path planning unit generates the shortest path by scanning environmental information within the warehouse area with LiDAR and updates navigation instructions in real time. The gripping robotic arm is installed on an automated handling device to grip or release tools at a designated location, and its end is equipped with flexible grippers to adapt to tools of different shapes and materials.

[0008] Preferably, the visual recognition module includes a 3D camera, an image processing unit, and a feature matching unit; The 3D camera is installed at the end of the gripping robotic arm to acquire the spatial position information of the tool in order to assist the gripping robotic arm in precise positioning. The image processing unit is used to perform noise reduction, edge detection, and feature enhancement on the tool images acquired by the 3D camera. The feature matching unit is used to compare the features of the acquired tool image with those of the tool image stored in the database to determine the type and state of the tool.

[0009] Preferably, the tool access and verification module includes a dynamic weighing unit, an environmental monitoring unit, and a data comparison unit; The dynamic weighing unit is installed at the bottom of the intelligent single-unit library to detect the weight change of the single-unit library after the tools are stored in real time. The environmental monitoring unit is used to monitor the temperature and humidity parameters inside the single-unit storage to ensure that the tool storage conditions meet the requirements; The data comparison unit is used to compare the actual weight detected by the dynamic weighing unit with the theoretical weight stored in the weight information processing unit to verify the accuracy of the quantity accessed by the tool.

[0010] Preferably, it also includes a tool classification and organization module, which is used to perform a secondary check on the type and quantity of tools during the tool storage and retrieval process; The tool classification and sorting module includes an image acquisition unit, a pattern recognition unit, and a counting unit; The image acquisition unit is installed at the end of the gripping robotic arm to acquire multi-angle images of the tool; The pattern recognition unit is used to classify and recognize the acquired images by combining the tool feature information in the database. The counting unit is used to count the number of tools by analyzing the distribution of tools in the image.

[0011] An operational method for a vision-based intelligent warehouse management system includes the following operating conditions: The method for the tool inclusion phase is as follows: S1: The visual recognition module is used to train newly added tools, extract their appearance features, and store them in the database; S2: Start the automated handling equipment to scan the storage area using the path planning unit to determine the target location for storing the tools; S3: The task scheduling unit communicates with the vision recognition module to obtain relevant information about the tool to be stored and queries the database to determine the best storage location for the tool. The automated handling equipment transports the tool to the target location and the gripping robotic arm places it in the intelligent single-unit library. S4: The dynamic weighing unit detects the weight changes of individual warehouse units in real time and determines whether the actual quantity received is consistent with the planned quantity by calculating the ratio of the weight increment to the weight of a single tool.

[0012] An operational method for a vision-based intelligent warehouse management system, tool outbound phase: S5: The user inputs the tool outbound list through the task scheduling unit; S6: The task scheduling unit generates outbound tasks based on the list content and schedules automated handling equipment to the designated location; S7: The task scheduling unit notifies the intelligent single-unit library to move the target tool to the outbound port, and the gripping robotic arm grabs the tool from the outbound port and places it on the automated handling equipment. S8: Automated material handling equipment transports tools to the user-designated pick-up point.

[0013] Preferably, if the dynamic weighing unit detects a discrepancy between the actual weight and the theoretical weight during the tool warehousing process, the image acquisition unit performs a second image acquisition on the tool, the pattern recognition unit analyzes the acquired image to determine whether other types of tools have been mixed in, and the counting unit recounts the number of tools.

[0014] Preferably, during the tool outbound process, when a specific number of tools need to be taken out, the dynamic weighing unit calculates the actual outbound quantity by detecting the decrease in the weight of the individual warehouse and combining it with the weight of the individual tools. If the calculation result does not match the planned quantity, an alarm mechanism is triggered and the outbound operation is suspended.

[0015] Preferably, in step S3, the repetitive task scheduling unit communicates with the visual recognition module to obtain relevant information about the tools to be stored and queries the database to determine the best storage location for the tools. The automatic handling equipment transports the tools to the target location and the gripping robotic arm places them in the intelligent single-unit library until all tools have completed the storage operation.

[0016] Preferably, in step S7, the repetitive task scheduling unit notifies the intelligent single-unit library to move the target tool to the outbound port, and the gripping robotic arm grabs the tool from the outbound port and places it on the automated handling equipment until all tools have completed the outbound operation.

[0017] The present invention can achieve the following beneficial effects: 1. This system integrates a visual recognition module, a tool scheduling and execution module, and automated handling equipment (such as AGVs and robotic grippers) to automate the entire process of tools from warehousing and storage to retrieval, reducing manual intervention. Especially in scenarios with a wide variety of tools and complex operations, such as turbine maintenance, it can significantly reduce the intensity of manual labor, avoid human error, and improve management efficiency.

[0018] 2. The system employs a dual verification mechanism of visual recognition and dynamic weighing to verify the type and quantity of tools in real time. If the weight does not match upon receipt, a secondary image acquisition and recognition can be performed through the tool classification and sorting module to check whether other tools have been mixed in. If the quantity deviates upon issuance, the system will trigger an alarm and pause operations to ensure the accuracy of inventory data and solve the problem of "discrepancy between records and actual inventory" in traditional management.

[0019] 3. The storage location allocation unit in the tool information management module calculates the optimal storage location based on the tool's size, weight, and usage frequency. This ensures that frequently used tools are placed near entrances and exits, and that large tools are rationally arranged within the designated space, significantly improving the utilization rate of storage space. Simultaneously, the path planning unit generates the shortest path in real time using LiDAR, ensuring efficient movement of automated handling equipment and reducing tool storage and retrieval time. Attached Figure Description

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the overall structure of the vision recognition-based intelligent warehouse management system of the present invention; Figure 2 This is a schematic diagram showing the composition and connection relationship of the information management module of the tool of the present invention; Figure 3 This is a schematic diagram of the workflow of the visual recognition module of the present invention; Figure 4 This is a schematic diagram illustrating the working principle of the access and verification module of the present invention. Figure 5 This is a schematic diagram of the secondary verification process of the tool classification and sorting module of the present invention; Figure 6 This is a schematic diagram of the tool entry operation process of the present invention. Detailed Implementation

[0021] A vision-based intelligent warehouse management system, with... Figure 1 This diagram illustrates the overall structure of the intelligent warehouse management system, showing the connections between the tool information management module, tool scheduling and execution module, visual recognition module, tool access verification module, and data analysis module. Each module interacts with other modules through a data communication interface. The tool information management module, as the core control unit, maintains real-time communication with other modules. The tool scheduling and execution module is responsible for executing physical operations. The visual recognition module identifies tool types and statuses. The tool access verification module ensures the accuracy of tool quantity and type. The data analysis module performs statistical analysis of historical data and optimizes management strategies.

[0022] The tool information management module includes a tool attribute input unit, a storage location allocation unit, an image feature extraction unit, a weight information processing unit, and a task scheduling unit. The tool attribute input unit records basic tool information, such as name, model, and specifications, through manual input or scanning and stores this information in a database. The storage location allocation unit calculates the optimal storage location based on the tool's volume, weight, and usage frequency, and associates this location information with the tool information. The image feature extraction unit captures images of the tool's appearance using a 3D camera and extracts visual features such as shape and color as identification criteria. The weight information processing unit measures the tool's weight using a high-precision electronic scale and stores the weight data in the database. The task scheduling unit receives user-input tool entry / exit commands and sends specific operation tasks to the tool scheduling execution module. The tool information management module and the tool scheduling execution module communicate via a wireless communication protocol to translate task commands into specific execution actions.

[0023] The tool scheduling and execution module includes an automated guided vehicle (AGV), a path planning unit, and a gripping robotic arm. The AGV moves along a preset path and transports tools to designated locations. The path planning unit uses LiDAR to scan the environmental information within the storage area, generating the shortest path and updating navigation instructions in real time. The gripping robotic arm is mounted on the AGV, its end effector equipped with flexible grippers to accommodate tools of different shapes and materials. The gripping robotic arm consists of a multi-joint mechanical structure and is driven by servo motors to achieve multi-degree-of-freedom movement. The path planning unit connects to the AGV via a CAN bus to transmit path information in real time. The gripping robotic arm and the AGV are fixed together via a mechanical interface and communicate via an electrical interface to complete the gripping and releasing actions of the tools.

[0024] The visual recognition module includes a 3D camera, an image processing unit, and a feature matching unit. The 3D camera is mounted on the end effector of the gripping robotic arm to acquire the spatial position information of the tool, assisting the robotic arm in precise positioning. The image processing unit preprocesses the tool images acquired by the 3D camera, including noise reduction, edge detection, and feature enhancement. The feature matching unit determines the type and state of the tool by comparing the acquired tool images with the tool image features stored in the database. The 3D camera and the image processing unit are connected via a data cable to transmit the acquired image information to the image processing unit for processing. The feature matching unit and the image processing unit are connected via an internal bus to achieve image feature comparison and result output.

[0025] The tool storage and verification module includes a dynamic weighing unit, an environmental monitoring unit, and a data comparison unit. The dynamic weighing unit, installed at the bottom of the intelligent single-unit storage unit, monitors real-time weight changes after tools are stored. The environmental monitoring unit monitors internal environmental parameters such as temperature and humidity to ensure tool storage conditions meet requirements. The data comparison unit verifies the accuracy of tool storage and retrieval by comparing the actual weight detected by the dynamic weighing unit with the theoretical weight stored in the weight information processing unit. The dynamic weighing unit and the data comparison unit are connected via a data communication interface for real-time weight data transmission. The environmental monitoring unit and the data comparison unit are connected via an internal network to share environmental parameters and monitor storage conditions.

[0026] The tool classification and sorting module includes an image acquisition unit, a pattern recognition unit, and a counting unit. The image acquisition unit, mounted at the end of the gripping robotic arm, acquires multi-angle images of the tools. The pattern recognition unit classifies and identifies the acquired images by combining tool feature information from a database. The counting unit analyzes the distribution of tools in the images to count the number of tools. The image acquisition unit and the pattern recognition unit are connected via a data cable to transmit the acquired image information to the pattern recognition unit for processing. The counting unit and the pattern recognition unit are connected via an internal bus to achieve tool count and result output.

[0027] In actual operation, during the tool receiving phase, the visual recognition module first trains the newly received tools to extract their appearance features and stores them in the database. The automated handling equipment is then activated, and the path planning unit scans the storage area to determine the target location for the tools. The task scheduling unit communicates with the visual recognition module to obtain relevant information about the tools to be stored and queries the database to determine the optimal storage location. The automated handling equipment transports the tools to the target location, and a gripping robotic arm places them into the intelligent single-unit storage compartment. The dynamic weighing unit monitors the weight changes of the single-unit storage compartment in real time. After a tool is placed in, the ratio of the weight increment to the weight of a single tool is calculated to determine whether the actual number of tools received matches the planned number. If a discrepancy is detected between the actual and theoretical weights, the image acquisition unit performs secondary image acquisition of the tools. The pattern recognition unit analyzes the acquired images to determine if other types of tools have been mixed in, and the counting unit recounts the tool count.

[0028] During the tool outbound phase, the user inputs an outbound tool list through the task scheduling unit. The task scheduling unit generates an outbound task based on the list and dispatches automated guided vehicles (AGVs) to the designated location. The task scheduling unit instructs the intelligent individual warehouse to move the target tool to the outbound port, where a robotic arm picks up the tool and places it on the AAV. The AAV transports the tool to the user-designated pickup point. During the outbound process, the dynamic weighing unit calculates the actual outbound quantity by detecting the decrease in weight of the individual warehouse and combining this with the weight of the individual tools. If the calculated result does not match the planned quantity, an alarm mechanism is triggered, and the outbound operation is suspended.

[0029] In the above embodiments, all modules and units achieve their specific functions through the collaborative work of hardware devices and software algorithms. Hardware devices include 3D cameras, high-precision electronic scales, automated handling equipment with gripping robotic arms, and various sensors. Software algorithms include image processing algorithms, feature matching algorithms, path planning algorithms, and data analysis algorithms. The connections between modules are achieved through data communication interfaces and internal buses to ensure real-time information transmission and efficient processing.

[0030] The system operates as follows: During the tool receiving phase, the spatial location information of newly received tools is first acquired using a 3D camera in the vision recognition module. The image processing unit performs noise reduction and edge detection on the received image data. Subsequently, the feature matching unit compares the processed image with the tool image features stored in the database to confirm the tool type and status. If it is a new tool, the tool attribute entry unit records its name, model, specifications, and other basic information, and the image feature extraction unit generates corresponding shape and color feature data which is then stored in the database. At this point, the storage location allocation unit calculates the optimal storage location based on the tool's volume, weight, and usage frequency, and associates this location information with the tool information. Upon receiving the receiving instruction, the task scheduling unit sends a transportation task to the automated handling equipment. The path planning unit scans the warehouse area environment using LiDAR, generates the shortest path, and transmits navigation instructions to the automated handling equipment in real time. The gripping robotic arm, with the assistance of flexible grippers, grasps the tool and places it in the designated intelligent single-unit storage area. The dynamic weighing unit monitors the weight changes of individual tools in real time. After a tool is placed in the warehouse, the ratio of the weight increment to the weight of a single tool is calculated to determine whether the actual quantity received matches the planned quantity. If a weight discrepancy is detected, the image acquisition unit captures multi-angle images of the tool, the pattern recognition unit classifies and identifies the captured images using tool feature information from the database, and the counting unit recounts the number of tools to ensure the accuracy of the warehouse data.

[0031] During the tool outbound phase, the user inputs an outbound list through the task scheduling unit. The task scheduling unit generates an outbound task based on the list and dispatches automated handling equipment (AWTA) to the designated location. The path planning unit scans the warehouse area environment again using LiDAR, generates the optimal path, and updates navigation instructions in real time. A robotic arm grasps the target tool from the intelligent individual storage unit and places it on the AWTA. The AWTA transports the tool along a preset path to the user-designated pick-up point. During the tool outbound process, a dynamic weighing unit continuously monitors the decrease in weight of the individual storage units and calculates the actual outbound quantity based on the weight of each tool. If the calculated quantity does not match the planned quantity, the data comparison unit triggers an alarm mechanism and suspends the outbound operation until the problem is resolved.

[0032] Throughout the storage and retrieval process, the environmental monitoring unit continuously monitors environmental parameters such as temperature and humidity within the individual storage unit to ensure that tool storage conditions meet requirements. The data analysis module optimizes warehouse management strategies through statistical analysis of historical inbound and outbound data. For example, it adjusts storage location allocation strategies based on tool usage frequency, prioritizing the placement of frequently used tools near entrances and exits to further improve storage and retrieval efficiency.

[0033] In the above embodiments, the collaborative operation between modules is achieved through a combination of hardware devices and software algorithms. For example, the 3D camera is connected to the image processing unit via a data cable, transmitting the acquired image information to the image processing unit for processing; the feature matching unit is connected to the image processing unit via an internal bus to achieve image feature comparison and result output. The dynamic weighing unit and the data comparison unit are connected via a data communication interface to ensure real-time transmission of weight data. All information interaction between modules is achieved through the data communication interface and the internal bus, ensuring the efficiency and real-time nature of information transmission.

[0034] Through the specific steps described above, the intelligent warehouse management system automates the entire tool storage and retrieval process. The visual recognition module and the tool storage and verification module work together, using both image recognition and weight detection to ensure accurate recording of tool types and quantities. The storage location allocation unit scientifically plans storage locations based on tool size, weight, and usage frequency, avoiding space waste and reducing warehousing costs. Simultaneously, the path planning unit, in conjunction with automated handling equipment, significantly shortens tool storage and retrieval time and improves overall operational efficiency.

[0035] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A vision-based intelligent warehouse management system, characterized in that: It includes a tool information management module, a tool scheduling and execution module, a visual recognition module, a tool access verification module, and a data analysis module; The tool information management module is used to record basic tool information and generate tool entry and exit instructions; The tool scheduling and execution module is used to complete the storage and retrieval operations of tools according to the tool entry and exit instructions; The visual recognition module is used to identify the type of tool during tool access. The tool access verification module is used to verify the quantity and type of tools during the tool access process; The data analysis module is used to perform statistical analysis on the historical data of tools entering and leaving the database.

2. The intelligent warehouse management system based on visual recognition according to claim 1, characterized in that: The tool information management module includes a tool attribute input unit, a storage location allocation unit, an image feature extraction unit, a weight information processing unit, and a task scheduling unit; The tool attribute input unit is used to record the name, model, and specifications of the tool and store this information in the database; The image feature extraction unit acquires the appearance image of the tool using a 3D camera and extracts shape and color features as a basis for recognition. The weight information processing unit measures the weight of the tool using a high-precision electronic scale and stores the weight data in a database. The storage location allocation unit is used to calculate the optimal storage location based on the tool's size, weight, and usage frequency, and then associate and store this location information with the tool information. The task scheduling unit is used to receive tool entry / exit instructions from the user and send specific operation tasks to the tool scheduling and execution module.

3. The intelligent warehouse management system based on visual recognition according to claim 1, characterized in that: The tool scheduling and execution module includes an automated handling device, a path planning unit, and a gripping robotic arm; The automated handling equipment uses AGVs to move along a preset path within the storage area and transport tools to a designated location; The path planning unit generates the shortest path by scanning environmental information within the warehouse area with LiDAR and updates navigation instructions in real time. The gripping robotic arm is installed on an automated handling device to grip or release tools at a designated location, and its end is equipped with flexible grippers to adapt to tools of different shapes and materials.

4. The intelligent warehouse management system based on visual recognition according to claim 1, characterized in that: The visual recognition module includes a 3D camera, an image processing unit, and a feature matching unit. The 3D camera is installed at the end of the gripping robotic arm to acquire the spatial position information of the tool in order to assist the gripping robotic arm in precise positioning. The image processing unit is used to perform noise reduction, edge detection, and feature enhancement on the tool images acquired by the 3D camera. The feature matching unit is used to compare the features of the acquired tool image with those of the tool image stored in the database to determine the type and state of the tool.

5. The intelligent warehouse management system based on visual recognition according to claim 1, characterized in that: The tool access and verification module includes a dynamic weighing unit, an environmental monitoring unit, and a data comparison unit; The dynamic weighing unit is installed at the bottom of the intelligent single-unit library to detect the weight change of the single-unit library after the tools are stored in real time. The environmental monitoring unit is used to monitor the temperature and humidity parameters inside the single-unit storage to ensure that the tool storage conditions meet the requirements; The data comparison unit is used to compare the actual weight detected by the dynamic weighing unit with the theoretical weight stored in the weight information processing unit to verify the accuracy of the quantity accessed by the tool.

6. The intelligent warehouse management system based on visual recognition according to claim 1, characterized in that: It also includes a tool classification and organization module, which is used to perform a secondary check on the type and quantity of tools during the tool storage and retrieval process; The tool classification and sorting module includes an image acquisition unit, a pattern recognition unit, and a counting unit; The image acquisition unit is installed at the end of the gripping robotic arm to acquire multi-angle images of the tool; The pattern recognition unit is used to classify and recognize the acquired images by combining the tool feature information in the database. The counting unit is used to count the number of tools by analyzing the distribution of tools in the image.

7. The operation method of a vision-based intelligent warehouse management system according to any one of claims 1-6, characterized in that... Including the following working conditions: The method for the tool inclusion phase is as follows: S1: The visual recognition module is used to train newly added tools, extract their appearance features, and store them in the database; S2: Start the automated handling equipment to scan the storage area using the path planning unit to determine the target location for storing the tools; S3: The task scheduling unit communicates with the vision recognition module to obtain relevant information about the tool to be stored and queries the database to determine the best storage location for the tool. The automated handling equipment transports the tool to the target location and the gripping robotic arm places it in the intelligent single-unit library. S4: The dynamic weighing unit detects the weight changes of individual warehouse units in real time and determines whether the actual quantity received is consistent with the planned quantity by calculating the ratio of the weight increment to the weight of a single tool.

8. The operation method of a vision-based intelligent warehouse management system according to claim 7, characterized in that: Tool outbound phase: S5: The user inputs the tool outbound list through the task scheduling unit; S6: The task scheduling unit generates outbound tasks based on the list content and schedules automated handling equipment to the designated location; S7: The task scheduling unit notifies the intelligent single-unit library to move the target tool to the outbound port, and the gripping robotic arm grabs the tool from the outbound port and places it on the automated handling equipment. S8: Automated material handling equipment transports tools to the user-designated pick-up point.

9. The operation method of a vision-based intelligent warehouse management system according to claim 7, characterized in that: If the dynamic weighing unit detects a discrepancy between the actual weight and the theoretical weight during the tool warehousing process, the image acquisition unit performs a second image acquisition of the tool. The pattern recognition unit analyzes the acquired image to determine whether other types of tools have been mixed in, and the counting unit recounts the number of tools.

10. The operation method of a vision-based intelligent warehouse management system according to claim 7, characterized in that: During the tool outbound process, when a specific number of tools need to be retrieved, the dynamic weighing unit calculates the actual outbound quantity by detecting the decrease in the weight of individual warehouses and combining it with the weight of individual tools. If the calculated result does not match the planned quantity, an alarm mechanism is triggered and the outbound operation is suspended.

11. The operation method of a vision-based intelligent warehouse management system according to claim 7, characterized in that: In step S3, the repetitive task scheduling unit communicates with the visual recognition module to obtain relevant information about the tools to be stored and queries the database to determine the best storage location for the tools. The automatic handling equipment transports the tools to the target location and the gripping robotic arm places them in the intelligent single-unit library until all tools have completed the storage operation.

12. The operation method of a vision-based intelligent warehouse management system according to claim 7, characterized in that: In step S7, the repetitive task scheduling unit notifies the intelligent single-unit library to move the target tool to the outbound port, and the gripping robotic arm grabs the tool from the outbound port and places it on the automated handling equipment until all tools have completed the outbound operation.

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