Intelligent warehouse logistics teaching sand table system

Abstract

The invention discloses an intelligent warehouse logistics teaching sand table system, and the system comprises the following modules: an order management module which is used for receiving and analyzing a teaching simulation order in a JSON format through a WebSocket protocol, and generating a material demand task containing a material SKU code, a batch number and an emergency degree; the path planning module adopts an improved Dijkstra algorithm to calculate an optimal carrying path considering the time constraint; the equipment control module is used for driving the AGV trolley and the six-degree-of-freedom mechanical arm in the sand table through a CANopen protocol; the state monitoring module is integrated with an RFID, a laser radar and an inertial measurement unit IMU; the central processing module adopts an ROS robot operating system architecture; the teaching evaluation module automatically generates an evaluation report containing a radar map and a trend curve; according to the intelligent warehouse logistics teaching sand table system, immersive teaching experience of virtual-real fusion is provided, digital twinning and entity sand table millisecond-level synchronization is achieved, a Unity 3D engine supports four interaction modes, an SIL2 safety level control interface guarantees operation safety, and a full-cycle learning process is supported.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an intelligent warehousing and logistics teaching sandbox system. Background Art

[0002] With the rapid development of Internet of Things technology, smart hardware devices have been widely used in various fields.

[0003] The existing teaching sand table has the following technical defects:

[0004] Data fragmentation: Subsystems such as order management and equipment control operate independently, resulting in data synchronization delays of up to 200-500ms.

[0005] Simple algorithm: Path planning only considers the static shortest path and does not incorporate practical constraints such as time windows;

[0006] Lack of evaluation: 90% of systems only record completion time and lack quantitative analysis of the operation process;

[0007] Difficulty in expansion: Developed in a hard-coded manner, the entire system needs to be recompiled to add new devices; and there is a lack of effective interaction with students. Students can only passively observe and find it difficult to actively participate in the teaching process. Their hands-on skills are not fully exercised, and the requirements for talent training in modern intelligent warehousing and logistics teaching cannot be met. Therefore, it is of great practical significance to develop an intelligent warehousing and logistics teaching sandbox system that is comprehensive in functions, highly interactive, and can simulate real scenarios. To this end, we propose an intelligent warehousing and logistics teaching sandbox system. Summary of the Invention

[0008] The purpose of the present invention is to provide an intelligent warehousing and logistics teaching sandbox system in response to the above situation, which effectively solves the related problems in the background technology.

[0009] The specific solution of the present invention is: an intelligent warehousing and logistics teaching sandbox system, including the following modules:

[0010] The order management module is used to receive and parse the teaching simulation order in JSON format through the WebSocket protocol, and generate material demand tasks containing material SKU code, batch number and urgency;

[0011] The path planning module uses the improved Dijkstra algorithm to calculate the optimal transportation path considering time constraints based on the BIM format warehouse 3D model and UWB real-time positioning data;

[0012] The equipment control module drives the AGV and six-degree-of-freedom robotic arm in the sandbox through the CANopen protocol, with a control response time of ≤50ms;

[0013] Condition monitoring module, integrating RFID, lidar and inertial measurement unit (IMU), with a sampling frequency of ≥100Hz;

[0014] The central processing module adopts the ROS robot operating system architecture and supports two communication modes: topic and service between modules;

[0015] The teaching evaluation module has a built-in machine learning-based evaluation algorithm that automatically generates evaluation reports including radar charts and trend curves;

[0016] The above modules communicate through the standard OPC UA interface to form a closed-loop control system with self-correction function.

[0017] Furthermore, the order management module and the route planning module interact with each other, specifically including:

[0018] The order management module encapsulates tasks into Protobuf format data packets, including the three-dimensional coordinates (x, y, z) of the shelf, the material classification code, and the dynamic priority weight;

[0019] The path planning module integrates the AGV's real-time positioning with map construction SLAM data and dynamic obstacle information to generate a time-optimal path with buffer time;

[0020] The order management module and the path planning module adopt the publish-subscribe mode of the MQTT protocol, supporting reliable transmission and task preemption mechanism with QoS level 1.

[0021] Furthermore, the path planning module and the device control module in the present invention cooperate to specifically include:

[0022] The path planning module outputs a smooth path containing a Bézier curve and an S-shaped velocity planning curve;

[0023] The equipment control module converts the instructions into PWM signals through the fuzzy PID controller, with an adjustment accuracy of 0.1%;

[0024] When the AGV posture deviation exceeds the preset threshold, i.e., position ±2cm and angle ±1°, online replanning based on the artificial potential field method is triggered.

[0025] Furthermore, the status monitoring module in the present invention includes: an RFID shelf identification unit to verify the correctness of material placement; an infrared positioning unit to track the real-time position of the AGV; and an anomaly detection unit to send an alarm signal to the central processing module when the AGV power level is lower than a threshold or a collision occurs.

[0026] Furthermore, the teaching evaluation module of the present invention evaluates the operation effect in the following ways:

[0027] Establish a multi-dimensional evaluation system: efficiency, quality, and energy consumption (power consumption, equipment wear). The efficiency dimension includes task completion rate and average time; the quality dimension includes the number of errors and path optimization; and the energy consumption dimension includes power consumption and equipment wear.

[0028] The TOPSIS multi-objective decision-making algorithm is used to calculate the comprehensive score and compare and analyze it with the optimal solution in the expert database;

[0029] Generates interactive drill-down time-series diagrams that highlight key decision points and improvement suggestions.

[0030] Furthermore, the central processing module in the present invention realizes dynamic resource allocation: monitors the CPU and memory occupancy of each module; enables parallel processing of distributed computing nodes when the path planning calculation load is too high; detects the module communication status through the heartbeat packet mechanism, and automatically switches to the backup link when an abnormality occurs.

[0031] Furthermore, the present invention supports fault simulation teaching, specifically including: the central processing module can inject AGV loss of connection, shelf displacement, and sensor failure simulated faults; record the trainees' fault diagnosis response time and recovery strategy effectiveness; and mark typical error handling methods in the evaluation report.

[0032] Furthermore, the present invention also includes a digital twin interaction module:

[0033] High-fidelity virtual models built on the Unity3D engine, with data synchronization delays of <100ms;

[0034] Provides four visualization modes: real-time monitoring mode, playback analysis mode, prediction simulation mode and teaching demonstration mode;

[0035] Three types of control interfaces are open: emergency braking interface, parameter adjustment interface, and teaching intervention interface; the emergency braking interface is SIL2 safety level, the parameter adjustment interface supports batch modification, and the teaching intervention interface requires dual authentication.

[0036] The intelligent warehousing and logistics teaching sandbox system of the present invention has the following beneficial effects:

[0037] (1) The intelligent warehousing and logistics teaching sandbox system realizes a closed-loop teaching environment with industrial-grade precision control. The present invention adopts the industrial standard CANopen protocol (response time ≤ 50ms) combined with fuzzy PID control (accuracy 0.1%) to achieve industrial-grade control accuracy. Multi-source sensor fusion (UWB+LiDAR+IMU) achieves high-precision positioning of ±2cm / ±1°. The dynamic replanning mechanism ensures that the AGV posture can be corrected in real time when it is out of tolerance. Students can experience the precise control requirements of real intelligent warehousing scenarios. Closed-loop feedback enables immediate visualization of operational errors, improves error correction efficiency, supports sub-second task response, and simulates a real industrial environment.

[0038] (2) The intelligent warehousing and logistics teaching sandbox system builds an intelligent multi-dimensional evaluation system. The present invention adopts the TOPSIS algorithm to realize the comprehensive evaluation of efficiency, quality and energy consumption in three dimensions. Machine learning automatically analyzes the operation data, identifies the error mode, links the fault simulation with the evaluation, and fully records the diagnosis and recovery process. It breaks through the traditional single time assessment and establishes a standard evaluation system for docking with the industry. The visual report accurately locates the operation error link, significantly improving the students' fault diagnosis and handling capabilities.

[0039] (3) The intelligent warehousing and logistics teaching sandbox system provides an immersive teaching experience that integrates virtual and real elements. The digital twin and the physical sandbox are synchronized at the millisecond level (delay <100ms). The Unity3D engine supports four interaction modes. The SIL2 safety level control interface ensures operational safety and supports the full-cycle learning process (practical operation-playback-prediction). It can visualize data dimensions that traditional sandboxes cannot present, and a complete permission management mechanism ensures teaching safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a system block diagram of the present invention;

[0041] Figure 2 This is a flow chart of path planning in the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings of the present invention. It is obvious that the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0043] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," and "connected" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0044] A preferred embodiment of the intelligent warehousing logistics teaching sandbox system provided by the present invention is as follows: Figure 1 、 Figure 2 As shown:

[0045] An intelligent warehousing and logistics teaching sandbox system includes the following modules:

[0046] The order management module is used to receive and parse the teaching simulation order in JSON format through the WebSocket protocol, and generate material demand tasks containing material SKU code, batch number and urgency;

[0047] The path planning module uses the improved Dijkstra algorithm to calculate the optimal transportation path considering time constraints based on the BIM format warehouse 3D model and UWB real-time positioning data;

[0048] The equipment control module drives the AGV and six-degree-of-freedom robotic arm in the sandbox through the CANopen protocol, with a control response time of ≤50ms;

[0049] Condition monitoring module, integrating RFID, lidar and inertial measurement unit (IMU), with a sampling frequency of ≥100Hz;

[0050] The central processing module adopts the ROS robot operating system architecture and supports two communication modes: topic and service between modules;

[0051] The teaching evaluation module has a built-in machine learning-based evaluation algorithm that automatically generates evaluation reports including radar charts and trend curves;

[0052] Each module communicates through a standard OPC UA interface to form a closed-loop control system with self-correction function.

[0053] Furthermore, the interaction between the order management module and the route planning module includes:

[0054] The order management module encapsulates tasks into Protobuf format data packets, including the three-dimensional coordinates (x, y, z) of the shelf, the material classification code, and the dynamic priority weight;

[0055] The path planning module integrates the AGV's simultaneous localization and mapping (SLAM) data and dynamic obstacle information to generate a time-optimal path with buffer time;

[0056] The two modules adopt the publish-subscribe mode of the MQTT protocol, supporting reliable transmission and task preemption mechanism with QoS level 1.

[0057] Furthermore, the collaboration between the path planning module and the equipment control module includes:

[0058] The path planning module outputs a smooth path containing a Bézier curve and an S-shaped velocity planning curve;

[0059] The equipment control module converts the instructions into PWM signals through the fuzzy PID controller, with an adjustment accuracy of 0.1%;

[0060] When the AGV posture deviation exceeds the preset threshold (position ±2cm, angle ±1°), online replanning based on the artificial potential field method is triggered.

[0061] Furthermore, the status monitoring module includes:

[0062] RFID shelf identification unit to verify the correctness of material placement;

[0063] Infrared positioning unit to track the real-time position of AGV;

[0064] The anomaly detection unit sends an alarm signal to the central processing module when the AGV power level drops below the threshold or a collision occurs.

[0065] Furthermore, the teaching evaluation module evaluates the operation effect in the following ways:

[0066] Establish a multi-dimensional evaluation system: efficiency dimension (task completion rate, average time), quality dimension (number of errors, path optimization), energy consumption dimension (power consumption, equipment wear);

[0067] The TOPSIS multi-objective decision-making algorithm is used to calculate the comprehensive score and compare and analyze it with the optimal solution in the expert database;

[0068] Generates interactive drill-down time-series diagrams that highlight key decision points and improvement suggestions.

[0069] Furthermore, the central processing module implements dynamic resource allocation: monitoring the CPU and memory usage of each module; enabling parallel processing of distributed computing nodes when the path planning calculation load is too high; detecting the module communication status through the heartbeat packet mechanism, and automatically switching to the backup link when an abnormality occurs.

[0070] Furthermore, it supports fault simulation teaching, including:

[0071] The central processing module can inject simulated faults such as AGV loss of connection, shelf displacement, and sensor failure; record the trainees' fault diagnosis response time and the effectiveness of the recovery strategy; and mark typical error handling methods in the evaluation report.

[0072] In addition, it also includes digital twin interaction modules:

[0073] High-fidelity virtual models built on the Unity3D engine, with data synchronization delays of <100ms;

[0074] It provides four visualization modes: real-time monitoring mode, playback analysis mode, predictive simulation mode, and teaching demonstration mode; and opens three types of control interfaces: emergency braking interface (SIL2 safety level), parameter adjustment interface (supports batch modification), and teaching intervention interface (requires dual authentication).

[0075] Working Principle: This system achieves industrial-grade precision control in a closed-loop teaching environment. It uses the industrial-standard CANopen protocol (response time ≤ 50ms) combined with fuzzy PID control (0.1% accuracy) to achieve industrial-grade control accuracy. Multi-source sensor fusion (UWB + LiDAR + IMU) achieves high-precision positioning of ±2cm / ±1°. A dynamic replanning mechanism ensures real-time correction of AGV posture deviations. Trainees can experience the precise control requirements of real smart warehousing scenarios. Closed-loop feedback enables immediate visualization of operational errors, improving error correction efficiency. It supports sub-second task response and simulates a real industrial environment.

[0076] Build an intelligent multi-dimensional evaluation system. This invention uses the TOPSIS algorithm to achieve a comprehensive evaluation of the three dimensions of efficiency, quality, and energy consumption. Machine learning automatically analyzes operational data, identifies error patterns, links fault simulation with evaluation, and fully records the diagnosis and recovery process. This breaks through the traditional single-time assessment and establishes a standard evaluation system that connects with the industry. Visual reports accurately locate operational errors, significantly improving trainees' fault diagnosis and handling capabilities.

[0077] It provides an immersive teaching experience that integrates virtual and real elements. The digital twin and the physical sandbox are synchronized with each other in milliseconds (delay <100ms). The Unity3D engine supports four interaction modes. The SIL2 safety-level control interface ensures operational safety and supports the full-cycle learning process (practical operation-playback-prediction). It can visualize data dimensions that traditional sandboxes cannot present, and a complete permission management mechanism ensures teaching safety.

[0078] The above description is only an illustrative embodiment of the present invention and is not intended to limit the scope of the present invention. Any equivalent changes and modifications made by any person skilled in the art without departing from the concept and principle of the present invention should fall within the scope of protection of the present invention. It should also be noted that the various components of the present invention are not limited to the above-mentioned overall application. The various technical features described in the specification of the present invention can be selected one by one or multiple by multiple combinations according to actual needs. Therefore, the present invention should naturally cover other combinations and specific applications related to this case.

Claims

1. An intelligent warehousing and logistics teaching sandbox system, characterized by , including the following modules: The order management module is used to receive and parse the teaching simulation order in JSON format through the WebSocket protocol, and generate material demand tasks containing material SKU code, batch number and urgency; The path planning module uses the improved Dijkstra algorithm to calculate the optimal transportation path considering time constraints based on the BIM format warehouse 3D model and UWB real-time positioning data; The equipment control module drives the AGV and six-degree-of-freedom robotic arm in the sandbox through the CANopen protocol, with a control response time of ≤50ms; Condition monitoring module, integrating RFID, lidar and inertial measurement unit (IMU), with a sampling frequency of ≥100Hz; The central processing module adopts the ROS robot operating system architecture and supports two communication modes: topic and service between modules; The teaching evaluation module has a built-in machine learning-based evaluation algorithm that automatically generates evaluation reports including radar charts and trend curves; The above modules communicate through the standard OPC UA interface to form a closed-loop control system with self-correction function.

2. The intelligent warehousing and logistics teaching sandbox system according to claim 1 is characterized in that: The order management module interacts with the route planning module, specifically including: The order management module encapsulates tasks into Protobuf format data packets, including the three-dimensional coordinates (x, y, z) of the shelf, the material classification code, and the dynamic priority weight; The path planning module integrates the AGV's real-time positioning with map construction SLAM data and dynamic obstacle information to generate a time-optimal path with buffer time; The order management module and the path planning module adopt the publish-subscribe mode of the MQTT protocol, supporting reliable transmission and task preemption mechanism with QoS level 1.

3. The intelligent warehousing and logistics teaching sandbox system according to claim 1 is characterized in that: The path planning module cooperates with the device control module, specifically including: The path planning module outputs a smooth path containing a Bézier curve and an S-shaped velocity planning curve; The equipment control module converts the instructions into PWM signals through the fuzzy PID controller, with an adjustment accuracy of 0.1%; When the AGV posture deviation exceeds the preset threshold, i.e., position ±2cm and angle ±1°, online replanning based on the artificial potential field method is triggered.

4. The intelligent warehousing logistics teaching sandbox system according to claim 1 is characterized in that: The status monitoring module includes: an RFID shelf identification unit to verify the correctness of material placement; an infrared positioning unit to track the real-time position of the AGV; and an anomaly detection unit to send an alarm signal to the central processing module when the AGV power level drops below a threshold or a collision occurs.

5. The intelligent warehousing and logistics teaching sandbox system according to claim 1 is characterized in that: The teaching evaluation module evaluates the operation effect in the following ways: Establish a multi-dimensional evaluation system: efficiency, quality, and energy consumption (power consumption, equipment wear). The efficiency dimension includes task completion rate and average time; the quality dimension includes the number of errors and path optimization; and the energy consumption dimension includes power consumption and equipment wear. The TOPSIS multi-objective decision-making algorithm is used to calculate the comprehensive score and compare and analyze it with the optimal solution in the expert database; Generates interactive drill-down time-series diagrams that highlight key decision points and improvement suggestions.

6. The intelligent warehousing and logistics teaching sandbox system according to claim 1 is characterized in that: The central processing module implements dynamic resource allocation: monitors the CPU and memory usage of each module; enables parallel processing of distributed computing nodes when the path planning calculation load is too high; detects the module communication status through the heartbeat packet mechanism, and automatically switches to the backup link when an abnormality occurs.

7. The intelligent warehousing and logistics teaching sandbox system according to claim 1 is characterized in that: Supports fault simulation teaching, including: the central processing module can inject simulated faults such as AGV loss of connection, shelf displacement, and sensor failure; record the trainees' fault diagnosis response time and the effectiveness of the recovery strategy; and mark typical error handling methods in the evaluation report.

8. An intelligent warehousing logistics teaching sandbox system according to claims 1-7, characterized in that: Also includes digital twin interaction modules: High-fidelity virtual models built on the Unity3D engine, with data synchronization delays of <100ms; Provides four visualization modes: real-time monitoring mode, playback analysis mode, prediction simulation mode and teaching demonstration mode; Three types of control interfaces are open: emergency braking interface, parameter adjustment interface, and teaching intervention interface; the emergency braking interface is SIL2 safety level, the parameter adjustment interface supports batch modification, and the teaching intervention interface requires dual authentication.

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