Three-dimensional positioning system and method based on stacker

Abstract

The invention relates to the technical field of intelligent warehouse logistics, in particular to a three-dimensional positioning system and method based on a stacker. The three-dimensional positioning system comprises a multi-source data acquisition module, a multi-source data fusion module, a three-dimensional space construction module, a communication module, a stacker control module and a three-dimensional space real-time updating module. The multi-source data acquisition module obtains spatial original data of the stacker from multiple dimensions, and the risk that a single data source is interfered by the external environment is reduced. The multi-source data fusion module fuses original data into six-degree-of-freedom pose information, accumulative errors of an encoder are avoided, a digital twinborn model constructed by the three-dimensional space construction module is combined with the communication module to achieve data interaction, and the stacker control module analyzes the digital twinborn model, generates an accurate PWM control signal and sends the accurate PWM control signal to the multi-source data fusion module. And the three-dimensional space real-time updating module continuously updates obstacle information and effectively compensates mechanical transmission gaps and deformation, so that the operation efficiency of the warehousing system and the cargo storage safety are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of intelligent warehousing and logistics technology, and in particular to a three-dimensional positioning system and method based on a stacker. Background Art

[0002] In automated warehousing systems, stacker cranes are core equipment responsible for the storage, retrieval, and handling of goods. Their positioning accuracy directly impacts the operational efficiency of the entire warehousing system and the storage safety of goods.

[0003] Traditional stacker crane positioning methods often rely on single sensor technologies, such as laser ranging and encoder positioning. While these methods can meet basic positioning needs to a certain extent, they have many limitations in practical applications. For example, laser ranging is easily affected by factors such as ambient light and dust, resulting in increased measurement errors. Encoder positioning is prone to cumulative errors after long-term operation and cannot effectively compensate for backlash and deformation during mechanical transmission.

[0004] In summary, the traditional stacker crane positioning system has problems such as measurement being easily disturbed by the external environment, encoders easily accumulating errors, and being unable to effectively compensate for gaps and deformations during mechanical transmission. This results in inaccurate positioning of the stacker crane, affecting the operating efficiency of the entire warehousing system and the storage safety of goods. Summary of the Invention

[0005] The purpose of the present invention is to provide a three-dimensional positioning system and method based on a stacker crane, aiming to solve the problems that the positioning system of the traditional stacker crane is easily affected by external environmental interference, the encoder easily accumulates errors and cannot effectively compensate for the gap and deformation during the mechanical transmission process, resulting in the positioning of the stacker crane being not accurate enough, which affects the operating efficiency of the entire warehousing system and the storage safety of the goods.

[0006] To achieve the above-mentioned object, the present invention provides a three-dimensional positioning system based on a stacker crane, comprising a multi-source data acquisition module, a multi-source data fusion module, a three-dimensional space construction module, a communication module, a stacker crane control module and a three-dimensional space real-time update module, wherein the output end of the multi-source data acquisition module is electrically connected to the input end of the multi-source data fusion module, the output end of the multi-source data fusion module is respectively connected to the three-dimensional space construction module and the communication module for bidirectional communication, the three-dimensional space construction module transmits control parameters to the stacker crane control module through the communication module, the output signal of the stacker crane control module drives the stacker crane actuator to move, and the input signal of the three-dimensional space real-time update module comes from the feedback data of the stacker crane control module and the real-time environmental data of the communication module;

[0007] The multi-source data acquisition module is used to obtain the original spatial data of the stacker in the X / Y / Z axes;

[0008] The multi-source data fusion module is used to fuse the original spatial data into six-degree-of-freedom posture information;

[0009] The three-dimensional space construction module constructs a digital twin model including a three-dimensional coordinate system of the shelf based on the six-degree-of-freedom posture information;

[0010] The communication module realizes data interaction between modules through the time-sensitive network protocol;

[0011] The stacker control module generates a PWM control signal for the stacker control system according to the digital twin model;

[0012] The three-dimensional space real-time update module continuously updates the obstacle information in the digital twin model through a dynamic voxel grid algorithm.

[0013] Among them, the multi-source data acquisition module includes a lidar array, a depth vision unit, an inertial navigation unit and an environmental perception sensor. The lidar array uses the reflection characteristics of lasers of different frequencies of a dual-frequency laser transmitter to achieve multi-modal precise ranging. The depth vision unit obtains high-precision three-dimensional point clouds by fusing structured light projection decoding data and ToF ranging data. The inertial navigation unit constructs a six-degree-of-freedom sensing network by combining MEMSIMU and fiber optic gyroscope data to perceive the motion posture of the stacker. The environmental perception sensor adopts a redundant configuration of millimeter-wave radar and ultrasonic sensor to ensure the reliability of environmental perception data.

[0014] Among them, the multi-source data fusion module includes a spatiotemporal alignment engine, an adaptive weighted fusion device and a fault diagnosis unit. The spatiotemporal alignment engine realizes nanosecond-level synchronization of multi-source data based on FPGA hardware to ensure the spatiotemporal consistency of data. The adaptive weighted fusion device uses quantum particle swarm optimization to dynamically adjust sensor weights to improve data fusion quality. The fault diagnosis unit uses the LSTM model to predict the health status of sensors and warn of faults in advance.

[0015] Among them, the three-dimensional space construction module includes a semantic segmentation processor, a topological map generator and a digital twin builder. The semantic segmentation processor uses an improved PointNet++ network to accurately identify the shelf topology structure and provide semantic information. The topological map generator complements the missing spatial data based on Gaussian process regression and generates a complete topological map. The digital twin builder generates a millimeter-level precision digital twin model through a multi-layer B-spline surface fitting algorithm.

[0016] Among them, the communication module includes a TSN switching core, a data compression unit and a security encryption module. The TSN switching core supports the IEEE 802.1Qbv standard to achieve low-latency, high-reliability time-sensitive communication. The data compression unit uses the Octree algorithm to efficiently compress point cloud data and reduce the transmission volume. The security encryption module integrates the national encryption SM4 and physical layer chaotic encryption dual mechanisms to ensure data transmission security.

[0017] The stacker crane control module includes a motion planner, a servo drive, and a safety monitoring unit. The motion planner combines an improved RRT* algorithm with Bezier curves to plan a smooth motion path. The servo drive uses a three-loop position / speed / current closed-loop system to precisely control the stacker crane's motion. The safety monitoring unit uses vibration spectrum analysis to diagnose the stacker crane's mechanical status in real time.

[0018] Among them, the three-dimensional space real-time update module includes a change detector, a voxel processor and a semantic predictor. The change detector quickly identifies dynamic obstacles in the three-dimensional space based on the improved DBSCAN algorithm. The voxel processor adopts a sparse octree structure to achieve local rapid update and improve update efficiency. The semantic predictor predicts the object's motion trajectory through a graph neural network and provides forward-looking information.

[0019] The present invention further provides a three-dimensional positioning method based on a stacker crane, which is applied to the three-dimensional positioning system based on the stacker crane as described above, and includes the following steps:

[0020] Start the multi-source data acquisition module to obtain the original spatial data of the stacker crane in the X / Y / Z axes to provide basic information for subsequent positioning processing;

[0021] The multi-source data fusion module receives the original spatial data and performs fusion processing to generate data that accurately reflects the six-degree-of-freedom position information of the stacker;

[0022] The three-dimensional space construction module constructs a digital twin model including a three-dimensional coordinate system of the shelf based on the six-degree-of-freedom posture information;

[0023] The communication module uses the time-sensitive network protocol to achieve bidirectional and stable transmission of digital twin models and instructions between modules;

[0024] The stacker crane control module analyzes the digital twin model and generates a PWM control signal to drive the stacker crane actuator to complete the task.

[0025] The present invention provides a three-dimensional positioning system and method based on a stacker, including a multi-source data acquisition module, a multi-source data fusion module, a three-dimensional space construction module, a communication module, a stacker control module and a three-dimensional space real-time update module. The multi-source data acquisition module obtains the spatial raw data of the stacker in the X / Y / Z three axes from multiple dimensions, reducing the risk of a single data source being interfered with by the external environment; the multi-source data fusion module fuses the raw data into six-degree-of-freedom posture information to avoid encoder cumulative errors; the digital twin model constructed by the three-dimensional space construction module can intuitively present the spatial state and realize data interaction in combination with the communication module; the stacker control module parses the digital twin model and generates a precise PWM control signal; and the three-dimensional space real-time update module uses a dynamic voxel grid algorithm to continuously update obstacle information, effectively compensate for mechanical transmission gaps and deformations, improve the positioning accuracy of the stacker, and ensure the operating efficiency of the warehousing system and the safety of cargo storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 This is a principle block diagram of the three-dimensional positioning system based on the stacker provided by the present invention.

[0028] Figure 2 It is a flowchart of the steps of the three-dimensional positioning method based on the stacker provided by the present invention.

[0029] 101-Multi-source data acquisition module, 102-Multi-source data fusion module, 103-Three-dimensional space construction module, 104-Communication module, 105-Stacker control module, 106-Three-dimensional space real-time update module, 107-LiDAR array, 108-Deep vision unit, 109-Inertial navigation unit, 110-Environmental perception sensor, 111-Spatiotemporal alignment engine, 112-Adaptive weighted fusion unit, 113-Fault diagnosis unit, 114-Semantic segmentation processor, 115-Topological map generator, 116-Digital twin builder, 117-TSN switching core, 118-Data compression unit, 119-Security encryption module, 120-Motion planner, 121-Servo drive, 122-Safety monitoring unit, 123-Change detector, 124-Voxel processor, 125-Semantic predictor. DETAILED DESCRIPTION

[0030] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0031] See also Figure 1 The present invention provides a three-dimensional positioning system based on a stacker crane, which includes a multi-source data acquisition module 101, a multi-source data fusion module 102, a three-dimensional space construction module 103, a communication module 104, a stacker crane control module 105 and a three-dimensional space real-time update module 106. The output end of the multi-source data acquisition module 101 is electrically connected to the input end of the multi-source data fusion module 102, and the output end of the multi-source data fusion module 102 is bidirectionally connected to the three-dimensional space construction module 103 and the communication module 104 respectively. The three-dimensional space construction module 103 transmits control parameters to the stacker crane control module 105 through the communication module 104. The output signal of the stacker crane control module 105 drives the stacker crane actuator to move. The input signal of the three-dimensional space real-time update module 106 comes from the feedback data of the stacker crane control module 105 and the real-time environmental data of the communication module 104.

[0032] The multi-source data acquisition module 101 is used to obtain the original spatial data of the stacker in the X / Y / Z axes;

[0033] The multi-source data fusion module 102 is used to fuse the spatial raw data into six-degree-of-freedom posture information;

[0034] The three-dimensional space construction module 103 constructs a digital twin model including a three-dimensional coordinate system of the shelf based on the six-degree-of-freedom posture information;

[0035] The communication module 104 implements data interaction between modules through a time-sensitive network protocol;

[0036] The stacker control module 105 generates a PWM control signal for the stacker control system according to the digital twin model;

[0037] The three-dimensional space real-time update module 106 continuously updates the obstacle information in the digital twin model through a dynamic voxel grid algorithm.

[0038] In this embodiment, the multi-source data acquisition module 101 obtains the spatial raw data of the stacker in the X / Y / Z three axes from multiple dimensions, reducing the risk of a single data source being interfered with by the external environment; the multi-source data fusion module 102 fuses the raw data into six-degree-of-freedom posture information to avoid encoder cumulative errors. The digital twin model constructed by the three-dimensional space construction module 103 can intuitively present the spatial state, and combined with the communication module 104 to realize data interaction, the stacker control module 105 parses the digital twin model and generates precise PWM control signals, and the three-dimensional space real-time update module 106 uses a dynamic voxel grid algorithm to continuously update obstacle information, effectively compensate for mechanical transmission gaps and deformations, improve the positioning accuracy of the stacker, and ensure the operating efficiency of the warehousing system and the safety of cargo storage.

[0039] Furthermore, the multi-source data acquisition module 101 includes a lidar array 107, a depth vision unit 108, an inertial navigation unit 109 and an environmental perception sensor 110. The lidar array 107 uses the reflection characteristic differences of lasers of different frequencies of a dual-frequency laser transmitter to achieve multi-modal precise ranging. The depth vision unit 108 obtains high-precision three-dimensional point clouds by fusing structured light projection decoding data and ToF ranging data. The inertial navigation unit 109 constructs a six-degree-of-freedom sensing network by combining MEMSIMU and fiber optic gyroscope data to perceive the motion posture of the stacker. The environmental perception sensor 110 uses a redundant configuration of millimeter-wave radar and ultrasonic sensors to ensure the reliability of environmental perception data.

[0040] In this embodiment, the lidar array 107 uses a dual-frequency laser transmitter to achieve multi-modal precise ranging by utilizing the difference in reflection characteristics; the depth vision unit 108 fuses structured light projection decoding and ToF ranging data to obtain a high-precision three-dimensional point cloud; the inertial navigation unit 109 combines MEMSIMU and fiber optic gyroscope data to construct a six-degree-of-freedom sensing network to perceive the motion posture of the stacker; the environmental perception sensor 110 adopts a redundant configuration of millimeter-wave radar and ultrasonic sensor, and ensures the reliability of environmental perception data through multi-mode collaboration, providing comprehensive and reliable data support for the precise positioning of the stacker.

[0041] Furthermore, the multi-source data fusion module 102 includes a spatiotemporal alignment engine 111, an adaptive weighted fusion unit 112 and a fault diagnosis unit 113. The spatiotemporal alignment engine 111 implements nanosecond-level synchronization of multi-source data based on FPGA hardware to ensure the spatiotemporal consistency of data. The adaptive weighted fusion unit 112 uses quantum particle swarm optimization to dynamically adjust sensor weights to improve data fusion quality. The fault diagnosis unit 113 uses an LSTM model to predict the health status of sensors and provide early warning of faults.

[0042] In this embodiment, the spatiotemporal alignment engine 111 relies on FPGA hardware to achieve nanosecond-level synchronization of multi-source data, effectively ensuring the spatiotemporal consistency of data and laying the foundation for subsequent fusion; the adaptive weighted fusion unit 112 uses the quantum particle swarm optimization algorithm to dynamically adjust the sensor weights, effectively improving the quality of data fusion and making the fusion results more accurate and reliable; the fault diagnosis unit 113 uses the LSTM model to predict the health status of the sensor and realize early warning of faults. The three work together to greatly enhance system stability and data availability, providing high-quality fused data for precise positioning of stackers.

[0043] Furthermore, the three-dimensional space construction module 103 includes a semantic segmentation processor 114, a topological map generator 115 and a digital twin builder 116. The semantic segmentation processor 114 uses an improved PointNet++ network to accurately identify the shelf topology structure and provide semantic information. The topological map generator 115 complements the missing spatial data based on Gaussian process regression to generate a complete topological map. The digital twin builder 116 generates a millimeter-level precision digital twin model through a multi-layer B-spline surface fitting algorithm.

[0044] In this embodiment, the semantic segmentation processor 114 utilizes an improved PointNet++ network to accurately identify shelf topology and extract semantic information, providing key details for spatial modeling. The topological map generator 115 utilizes Gaussian process regression to effectively complete missing spatial data, generating a complete and accurate topological map and ensuring the consistency of spatial information. The digital twin builder 116 employs a multi-layer B-spline surface fitting algorithm to generate a digital twin model with millimeter-level accuracy, highly reproducing the actual warehouse space. These three components work closely together to provide the stacker crane with a realistic, accurate, and complete three-dimensional spatial model, enabling precise positioning and efficient operation.

[0045] The mathematical expression of the multi-layer B-spline surface fitting algorithm is:

[0046]

[0047] Where B i,p (u) and B i,p (v) is the p-order and q-order B-spline basis functions, P ij is the control vertex, (u,v) is the parameter domain coordinate.

[0048] Furthermore, the communication module 104 includes a TSN switching core 117, a data compression unit 118 and a security encryption module 119. The TSN switching core 117 supports the IEEE 802.1Qbv standard to achieve low-latency, high-reliability time-sensitive communication. The data compression unit 118 uses the Octree algorithm to efficiently compress point cloud data and reduce the transmission volume. The security encryption module 119 integrates the national encryption SM4 and physical layer chaotic encryption dual mechanisms to ensure data transmission security.

[0049] In this embodiment, the TSN switching core 117 adheres to the IEEE 802.1Qbv standard, building a low-latency, highly reliable time-sensitive communication network to ensure the timeliness and stability of data transmission. The data compression unit 118 utilizes the Octree algorithm to efficiently compress point cloud data, significantly reducing the amount of transmitted data and improving transmission efficiency. The security encryption module 119 integrates the dual mechanisms of national SM4 encryption and physical layer chaotic encryption to ensure data transmission security at both the software and physical levels, preventing data leakage and tampering. These three elements work together to provide efficient, secure, and stable data communication for the stacker crane's three-dimensional positioning system.

[0050] Furthermore, the stacker control module 105 includes a motion planner 120, a servo driver 121 and a safety monitoring unit 122. The motion planner 120 combines the improved RRT* algorithm with the Bezier curve to plan a smooth motion path. The servo driver 121 uses a three-loop position / speed / current closed loop to precisely control the stacker motion. The safety monitoring unit 122 diagnoses the mechanical status of the stacker in real time through vibration spectrum analysis.

[0051] In this embodiment, the motion planner 120 combines an improved RRT* algorithm with Bezier curves to plan a smooth and efficient motion path, reducing pauses and jitter during the stacker's operation. The servo driver 121 utilizes a three-loop position / speed / current control system to precisely control the stacker's motion, ensuring accurate execution of the planned path. The safety monitoring unit 122 utilizes vibration spectrum analysis to monitor the stacker's mechanical status in real time, promptly identifying potential faults. These three components work together to ensure the stacker's stable, precise, and safe operation.

[0052] Furthermore, the three-dimensional space real-time update module 106 includes a change detector 123, a voxel processor 124 and a semantic predictor 125. The change detector 123 quickly identifies dynamic obstacles in the three-dimensional space based on the improved DBSCAN algorithm. The voxel processor 124 adopts a sparse octree structure to achieve local rapid updates and improve update efficiency. The semantic predictor 125 predicts the movement trajectory of objects through a graph neural network and provides forward-looking information.

[0053] In this embodiment, the change detector 123 utilizes an improved DBSCAN algorithm to quickly and accurately identify dynamic obstacles in three-dimensional space, providing a key basis for real-time updates. The voxel processor 124 employs a sparse octree structure to rapidly update local space, significantly improving update efficiency and ensuring the timeliness of information. The semantic predictor 125 utilizes a graph neural network to predict the movement trajectory of objects, providing the system with forward-looking information and facilitating pre-planning of response strategies. These three components work together to ensure that three-dimensional spatial information remains accurate, real-time, and forward-looking.

[0054] See also Figure 2 The present invention also provides a three-dimensional positioning method based on a stacker crane, which is applied to the three-dimensional positioning system based on the stacker crane as described above, and includes the following steps:

[0055] S1: Start the multi-source data acquisition module 101 to obtain the original spatial data of the stacker crane in the X / Y / Z axes to provide basic information for subsequent positioning processing;

[0056] S2: The multi-source data fusion module 102 receives and fuses the original spatial data to generate data that accurately reflects the six-degree-of-freedom position information of the stacker crane;

[0057] S3: The three-dimensional space construction module 103 constructs a digital twin model including a three-dimensional coordinate system of the shelf based on the six-degree-of-freedom posture information;

[0058] S4: The communication module 104 realizes bidirectional and stable transmission of digital twin models and instructions between modules through the time-sensitive network protocol;

[0059] S5: The stacker control module 105 analyzes the digital twin model and generates a PWM control signal to drive the stacker actuator to complete the task.

[0060] In this embodiment, the multi-source data acquisition module 101 obtains the spatial raw data of the stacker in the X / Y / Z three axes from multiple dimensions, reducing the risk of a single data source being interfered with by the external environment; the multi-source data fusion module 102 fuses the raw data into six-degree-of-freedom posture information to avoid encoder cumulative errors. The digital twin model constructed by the three-dimensional space construction module 103 can intuitively present the spatial state, and combined with the communication module 104 to realize data interaction, the stacker control module 105 parses the digital twin model and generates precise PWM control signals, and the three-dimensional space real-time update module 106 uses a dynamic voxel grid algorithm to continuously update obstacle information, effectively compensate for mechanical transmission gaps and deformations, improve the positioning accuracy of the stacker, and ensure the operating efficiency of the warehousing system and the safety of cargo storage.

[0061] The above disclosure is only a preferred embodiment of the present invention, and certainly cannot be used to limit the scope of the rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A three-dimensional positioning system based on a stacker crane, characterized in that: It includes a multi-source data acquisition module, a multi-source data fusion module, a three-dimensional space construction module, a communication module, a stacker control module and a three-dimensional space real-time update module. The output end of the multi-source data acquisition module is electrically connected to the input end of the multi-source data fusion module. The output end of the multi-source data fusion module is bidirectionally connected to the three-dimensional space construction module and the communication module respectively. The three-dimensional space construction module transmits control parameters to the stacker control module through the communication module. The output signal of the stacker control module drives the stacker actuator to move. The input signal of the three-dimensional space real-time update module comes from the feedback data of the stacker control module and the real-time environmental data of the communication module. The multi-source data acquisition module is used to obtain the original spatial data of the stacker in the X / Y / Z axes; The multi-source data fusion module is used to fuse the original spatial data into six-degree-of-freedom posture information; The three-dimensional space construction module constructs a digital twin model including a three-dimensional coordinate system of the shelf based on the six-degree-of-freedom posture information; The communication module realizes data interaction between modules through the time-sensitive network protocol; The stacker control module generates a PWM control signal for the stacker control system according to the digital twin model; The three-dimensional space real-time update module continuously updates the obstacle information in the digital twin model through a dynamic voxel grid algorithm.

2. The three-dimensional positioning system based on a stacker crane according to claim 1, characterized in that: The multi-source data acquisition module includes a lidar array, a depth vision unit, an inertial navigation unit and an environmental perception sensor. The lidar array uses the reflection characteristics of lasers of different frequencies from a dual-frequency laser transmitter to achieve multi-modal precise ranging. The depth vision unit obtains high-precision three-dimensional point clouds by fusing structured light projection decoding data with ToF ranging data. The inertial navigation unit constructs a six-degree-of-freedom sensing network by combining MEMS IMU and fiber optic gyroscope data to perceive the motion posture of the stacker. The environmental perception sensor uses a redundant configuration of millimeter-wave radar and ultrasonic sensors to ensure the reliability of environmental perception data.

3. The three-dimensional positioning system based on a stacker crane according to claim 2, characterized in that: The multi-source data fusion module includes a spatiotemporal alignment engine, an adaptive weighted fusion unit, and a fault diagnosis unit. The spatiotemporal alignment engine implements nanosecond-level synchronization of multi-source data based on FPGA hardware to ensure data spatiotemporal consistency. The adaptive weighted fusion unit uses quantum particle swarm optimization to dynamically adjust sensor weights to improve data fusion quality. The fault diagnosis unit uses an LSTM model to predict sensor health status and provide early warning of faults.

4. The three-dimensional positioning system based on a stacker crane according to claim 3, characterized in that: The three-dimensional space construction module includes a semantic segmentation processor, a topological map generator and a digital twin builder. The semantic segmentation processor uses an improved PointNet++ network to accurately identify the shelf topology structure and provide semantic information. The topological map generator complements missing spatial data based on Gaussian process regression and generates a complete topological map. The digital twin builder generates a millimeter-level precision digital twin model through a multi-layer B-spline surface fitting algorithm.

5. The three-dimensional positioning system based on a stacker crane according to claim 4, characterized in that: The communication module includes a TSN switching core, a data compression unit and a security encryption module. The TSN switching core supports the IEEE 802.1Qbv standard to achieve low-latency, high-reliability time-sensitive communication. The data compression unit uses the Octree algorithm to efficiently compress point cloud data and reduce transmission volume. The security encryption module integrates the national encryption SM4 and physical layer chaotic encryption dual mechanisms to ensure data transmission security.

6. The three-dimensional positioning system based on a stacker crane according to claim 5, characterized in that: The stacker crane control module includes a motion planner, a servo driver, and a safety monitoring unit. The motion planner combines an improved RRT* algorithm with Bezier curves to plan a smooth motion path. The servo driver uses a three-loop position / speed / current closed loop to precisely control the stacker crane's motion. The safety monitoring unit uses vibration spectrum analysis to diagnose the stacker crane's mechanical status in real time.

7. The three-dimensional positioning system based on a stacker crane according to claim 6, characterized in that: The three-dimensional space real-time update module includes a change detector, a voxel processor and a semantic predictor. The change detector quickly identifies dynamic obstacles in the three-dimensional space based on the improved DBSCAN algorithm. The voxel processor uses a sparse octree structure to achieve local rapid updates and improve update efficiency. The semantic predictor predicts the movement trajectory of objects through a graph neural network and provides forward-looking information.

8. A three-dimensional positioning method based on a stacker crane, applied to the three-dimensional positioning system based on a stacker crane as claimed in claim 1, characterized in that: The steps include: Start the multi-source data acquisition module to obtain the original spatial data of the stacker crane in the X / Y / Z axes to provide basic information for subsequent positioning processing; The multi-source data fusion module receives the original spatial data and performs fusion processing to generate data that accurately reflects the six-degree-of-freedom position information of the stacker; The three-dimensional space construction module constructs a digital twin model including a three-dimensional coordinate system of the shelf based on the six-degree-of-freedom posture information; The communication module uses the time-sensitive network protocol to achieve bidirectional and stable transmission of digital twin models and instructions between modules; The stacker crane control module analyzes the digital twin model and generates a PWM control signal to drive the stacker crane actuator to complete the task.

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