Automated goods warehousing method and system for intelligent warehousing

Abstract

The application discloses a goods automatic warehousing method and system for intelligent warehousing, and belongs to the technical field of intelligent warehousing. The method obtains dynamic event information such as goods elevator fault signal and robot position conflict detection data through a real-time data interface, constructs a dynamic path adaptive scheduling mechanism based on a path planning algorithm, and pre-provisions a path set, triggers an edge computing unit to complete a backup path calculation strategy within a system pre-provisioned time threshold when a dynamic event is detected, and finally controls the warehousing robot to execute a warehousing operation according to the planned path. The system comprises a data sensing module, a path planning module, an edge computing unit and an execution control module, and each module cooperates to realize real-time path optimization under dynamic events. The application effectively solves the problems of insufficient adaptability of dynamic scenes and low device cooperation efficiency in traditional warehousing, improves the warehousing efficiency and reduces the energy consumption.

Description

Technical Field

[0001] This invention relates to an automated goods receiving method and system for intelligent warehousing, belonging to the field of intelligent warehousing technology. Background Technology

[0002] With the booming development of e-commerce logistics and intelligent manufacturing, intelligent warehousing systems have become a core link in improving supply chain efficiency. Currently, automated goods receiving, as a key process in intelligent warehousing, faces technical challenges such as insufficient adaptability to dynamic scenarios and low equipment collaboration efficiency. Specifically, in traditional warehousing, cross-floor transport by warehouse robots relies on manual scheduling of freight elevators, resulting in independent operation of elevators and robots and a lack of data communication; static path planning struggles to handle dynamic events, leading to poor operational continuity; and in the receiving process, data from robots, freight elevators, conveyor lines, and other equipment are scattered across different systems, lacking a unified collaborative scheduling model. To address these issues, this invention proposes an automated goods receiving method and system for intelligent warehousing. Summary of the Invention

[0003] To address the problems of insufficient adaptability to dynamic scenarios, low equipment collaboration efficiency, and frequent manual intervention in existing intelligent warehousing inbound processes, this invention provides an automated goods inbound method and system for intelligent warehousing. Through multi-source data fusion, dynamic path planning, and equipment collaborative scheduling, it achieves real-time linkage between freight elevators and robots, improving inbound efficiency and reducing energy consumption. This invention is implemented using the following technical solutions.

[0004] On one hand, the present invention provides an automated goods receiving method for intelligent warehousing. The automated receiving method is applied to an automated receiving system, which controls a warehousing robot to receive goods and includes multiple edge computing units. The automated receiving method is characterized by: acquiring dynamic event information in a goods receiving scenario through a real-time data interface; when a dynamic event is detected, selecting at least one edge computing unit to jointly generate at least one alternative path for the warehousing robot based on the dynamic event information, the location information of the warehousing robot, and surrounding image information collected by the warehousing robot, wherein the processing time of the selected edge computing units is shorter than a system preset time; and controlling the warehousing robot to perform the receiving operation according to one of the alternative paths.

[0005] Furthermore, the real-time data interface includes an industrial communication protocol interface for data interaction with the elevator controller and the robot controller, and the dynamic event information includes elevator fault signals and robot position conflict detection data.

[0006] Furthermore, the freight elevator fault signal further includes: acquiring the operating status data of the freight elevator PLC through an industrial Ethernet interface, wherein the operating status data includes the freight elevator position, load weight, and door open / close status.

[0007] Furthermore, the robot position conflict detection data further includes: real-time acquisition of three-dimensional coordinate data of each warehouse robot through a positioning system, wherein the positioning accuracy of the positioning system meets the system's preset positioning error requirements; and acquisition of robot position conflict data through deployed lidar sensors, wherein the scanning frequency and angular resolution of the lidar meet the system's preset detection accuracy requirements.

[0008] Furthermore, after acquiring robot position conflict data through the positioning system and lidar sensor, the method further includes: synchronizing the acquired multi-source heterogeneous data with timestamps, wherein the multi-source heterogeneous data includes data acquired by the positioning system and data acquired by the lidar, and the timetamp synchronization error meets the system's preset synchronization accuracy requirements; processing the robot position data using a data fusion algorithm to eliminate random errors in the positioning system; and calculating conflict characteristic parameters such as the relative speed and minimum approach distance of adjacent robots based on the processed position data.

[0009] Furthermore, the dynamic path adaptive scheduling mechanism based on the path planning algorithm further includes: constructing a basic path planning model based on a graph search algorithm; introducing time window constraints to construct a multi-robot path conflict avoidance model; and optimizing and training the path planning model based on a machine learning algorithm.

[0010] Furthermore, the preset backup path set further includes: pre-generating at least three backup paths with different priorities based on the warehouse 3D map; the path priority is determined by at least two of the following: path length, energy consumption coefficient, equipment usage frequency, and congestion probability; when the main path is occupied or malfunctions, the backup path is selected in order of priority.

[0011] Furthermore, the edge computing unit executes a backup path calculation strategy, which further includes: parallel calculation of multiple candidate paths based on the multi-core processor of the edge computing unit; using a heuristic search algorithm to complete the optimal path selection within a preset time threshold of the system; and offloading some computing tasks to the cloud server when computing resources are insufficient.

[0012] Furthermore, the control of the warehousing robot to perform the warehousing operation according to the planned path further includes: generating robot motion control commands based on the path planning results, the commands including speed curves, turning angles, and acceleration parameters; transmitting the control commands to the warehousing robot in real time through an industrial wireless communication network; and using a closed-loop control algorithm to control the robot's motion trajectory, wherein the trajectory tracking error meets the system's preset accuracy requirements.

[0013] On the other hand, the present invention provides an automated goods receiving system for intelligent warehousing, comprising: a data sensing module for acquiring elevator malfunction signals and robot position conflict data through sensors; a path planning module with a built-in dynamic path adaptive scheduling mechanism and a set of backup paths, the set of backup paths including at least one backup path; an edge computing unit for calculating backup path strategies within a preset time threshold range when a dynamic event is detected; and an execution control module for controlling the warehousing robot to perform receiving operations according to the planned path; wherein the data sensing module, path planning module, edge computing unit, and execution control module work together to achieve real-time path optimization under dynamic events.

[0014] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0015] (1) Dynamic event information in the goods entry scenario is continuously obtained through the real-time data interface. When a dynamic event is detected, the edge computing unit collaboration mechanism is immediately activated, avoiding the transmission delay of the traditional central processing mode.

[0016] (2) Based on the dynamic event type and real-time environmental data, the edge computing unit collaboratively generates at least one backup path, effectively reducing the impact of dynamic events on the warehousing operation and ensuring the continuity of the goods warehousing process.

[0017] (3) The generated backup path takes into account factors such as path length and energy consumption coefficient, so that the warehouse robot consumes less energy when performing inbound operations. Attached Figure Description

[0018] Figure 1 The diagram shows a workflow for an automated goods receiving method for smart warehousing.

[0019] Figure 2 The diagram shown is a schematic of the planned route;

[0020] Figure 3 The diagram shows a structural schematic of an automated goods receiving system for smart warehousing. Detailed Implementation

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0024] This embodiment provides an automated goods receiving method for smart warehousing, such as... Figure 1 As shown, the automated inbound method is applied to an automated inbound system, which controls warehouse robots to inbound goods and includes multiple edge computing units. The automated inbound method includes:

[0025] S101. Obtain dynamic event information in the goods warehousing scenario through a real-time data interface;

[0026] S102. When a dynamic event is detected, at least one edge computing unit is selected to jointly generate at least one backup path for the warehouse robot based on the dynamic event information, the location information of the warehouse robot, and the surrounding image information collected by the warehouse robot. The processing time of the selected edge computing units is shorter than the system preset time.

[0027] S103. Control the warehouse robot to perform the inbound operation according to one of the alternative paths.

[0028] This embodiment acquires dynamic event information such as freight elevator fault signals and robot position conflict detection data through a real-time data interface. It constructs a dynamic path adaptive scheduling mechanism based on a path planning algorithm and presets a set of backup paths. When a dynamic event is detected, the edge computing unit is triggered to complete the backup path calculation strategy within the system's preset time threshold. Finally, it controls the warehouse robot to perform the inbound operation according to the planned path.

[0029] In this embodiment, dynamic event information is acquired through a real-time data interface, and the operating status of the freight elevator and robots is collected in real time by a monitoring system. Specifically, sensors are deployed at key parts of the freight elevator to monitor abnormal conditions during its operation. When the freight elevator exhibits fault signals such as vibration amplitude exceeding a threshold, abnormal current increase, or abnormal motor noise, the sensors immediately generate alarm signals. Simultaneously, positioning devices and LiDAR within the warehouse continuously monitor the spatial position of the robots. When two robots are about to occupy the same path at the same time, or when the distance between them is less than a safety threshold, the system generates conflict warning data in real time and transmits it to the central control system. In addition, the system pre-plans backup paths for the robots, constructing a dynamic path redundancy mechanism. Based on the three-dimensional layout of the warehouse, engineers pre-generate at least one backup path using an intelligent path planning algorithm. The planning of backup paths comprehensively considers multiple factors such as path length, energy consumption indicators, equipment usage frequency, and congestion probability, and assigns priority to each path based on these factors. For example, although the main path is the shortest path, backup path A can avoid high-congestion risk sections, while backup path B is suitable for freight elevator failure scenarios.

[0030] When a freight elevator malfunction or a collision risk is detected with the robot, the system immediately triggers the edge computing unit as the local intelligent control core. Based on an embedded computing architecture, this unit can initiate a backup path calculation strategy within a preset time threshold. Based on the urgency of the dynamic event and the actual situation on site, it quickly selects the optimal path from the backup path set. For example, when the freight elevator malfunctions, the system prioritizes routes that do not rely on it; if a robot path conflict is detected, it selects a path that enables rapid bidirectional obstacle avoidance. The local computing mode avoids cloud data transmission delays, achieving millisecond-level response through real-time decision-making at the edge nodes. After path planning is completed, the system sends a control data packet containing trajectory coordinates, speed parameters, and steering commands to the robot. Upon receiving the packet, the robot automatically reconstructs its motion trajectory. Simultaneously, the system monitors the robot's pose in real time using laser SLAM and inertial navigation fusion technology, dynamically adjusting motor output parameters using a PID closed-loop control algorithm to control trajectory tracking errors within millimeter-level range, ensuring the accuracy and safety of the warehousing operation.

[0031] This invention integrates technologies such as real-time data acquisition, dynamic path planning, edge computing, and equipment collaborative control. It constructs a comprehensive monitoring network using industrial sensors and communication protocols to achieve real-time perception of equipment failures and path conflicts; pre-generates multi-priority backup paths based on the warehouse topology to form an intelligent emergency response mechanism; utilizes edge computing nodes for local decision-making and path switching, eliminating remote communication delays; and finally, ensures robot execution efficiency through high-precision motion control algorithms. This solution effectively addresses dynamic scenarios such as freight elevator malfunctions and robot congestion, reducing manual intervention, improving goods receiving efficiency, and solving the technical problems of poor equipment coordination and slow response in traditional warehousing systems. Figure 2 .

[0032] Furthermore, the real-time data interface includes an industrial communication protocol interface for data interaction with the elevator controller and the robot controller, and the dynamic event information includes elevator fault signals and robot position conflict detection data.

[0033] In this embodiment, the industrial communication protocol interface of the intelligent warehousing system serves as a protocol conversion hub between heterogeneous devices, enabling command interaction and status feedback between the freight elevator and the robot control unit. By being compatible with multiple industrial communication protocols such as OPC UA, Modbus, and CANopen, a standardized data interaction channel is constructed, enabling the freight elevator controller and the robot controller to achieve real-time data communication through a unified interface.

[0034] Furthermore, the freight elevator fault signal obtains the operating status data of the freight elevator PLC through the industrial Ethernet interface. The operating status data includes the freight elevator position, load weight, and door open / close status.

[0035] In this embodiment, the freight elevator controller uses a programmable logic controller (PLC) to interact with PLCs from different manufacturers via an industrial communication protocol, directly reading operating parameters such as floor position and load weight. For PLCs of different brands, the system performs data format conversion using a standard protocol to ensure protocol compatibility and system recognizability across different manufacturers. The system collects vibration signals from the freight elevator motor in real time via an industrial Ethernet interface and reads data from the PLC's internal fault register. Once an abnormality is detected, the fault information is immediately transmitted to the system's core processing unit, providing data support for subsequent fault diagnosis and handling.

[0036] In terms of robot localization and communication, the system acquires the robot's three-dimensional coordinate data through high-precision positioning technology and uses standardized protocols to package and transmit the data. Simultaneously, it communicates with the robot controller via an industrial fieldbus protocol to obtain real-time status parameters such as robot battery level and operating mode, enabling comprehensive monitoring of the robot's operational status. When the system needs to issue new path instructions, data is transmitted via an IoT communication protocol with a retransmission mechanism to ensure reliable transmission of instructions even in environments with network fluctuations.

[0037] To address the protocol heterogeneity issue between the freight elevator and the robot, the system deploys a protocol conversion middleware. This middleware discovers the freight elevator nodes through an industrial protocol client and converts the data to a unified format using a protocol converter. All devices undergo clock calibration via a time synchronization protocol to prevent control misjudgments caused by time discrepancies. To ensure communication reliability, the system employs a dual-link redundancy design, with each device simultaneously connected to both an industrial Ethernet network and a wireless LAN. The system periodically sends heartbeat packets to check the online status of the devices; if no response is received consecutively, an early warning mechanism is triggered to ensure timely handling of communication anomalies. By updating the operational data of the freight elevator and the robot in real time, the system can dynamically monitor the equipment status, providing an accurate data foundation for path planning and fault handling.

[0038] Furthermore, the robot position conflict detection data includes:

[0039] S201. The three-dimensional coordinate data of each warehouse robot are acquired in real time through the positioning system, and the positioning accuracy of the positioning system meets the system's preset positioning error requirements.

[0040] S202. Robot position conflict data is acquired through the deployed lidar sensor, wherein the scanning frequency and angular resolution of the lidar meet the system's preset detection accuracy requirements.

[0041] In this embodiment, the system equips each warehouse robot with a three-dimensional spatial positioning system, dynamically tracking its spatial position in the warehouse environment through real-time coordinate acquisition technology. The positioning system employs ultra-wideband (UWB) technology or laser SLAM technology, enabling it to accurately output the robot's position data (X, Y, Z) in a three-dimensional coordinate system, for example, coordinates (10m, 5m, 2m) in the third-level plane coordinate system of the warehouse. The system presets a positioning error threshold to ensure that the position data accuracy meets the requirements of the path planning algorithm, avoiding path conflicts or operation interruptions due to positioning deviations.

[0042] Simultaneously, the system deploys LiDAR sensors to construct an environmental perception system, which performs a 360° scan of the surrounding environment at a fixed frequency and angular resolution. Through point cloud data processing technology, it can not only identify static obstacles (such as walls and shelves) but also track the spatial position of dynamic targets (such as other robots) in real time. The positioning system and LiDAR constitute the robot's environmental perception unit: the former provides its own precise coordinates, while the latter generates a real-time point cloud map of the surrounding environment.

[0043] When the LiDAR detects another robot entering the work area, the system performs multi-sensor data fusion processing, combining positioning data with the data to calculate the relative speed and minimum approach distance using a Kalman filter algorithm. For example, when robot M is detected approaching robot N at a speed of 0.8 m / s, and it is predicted that both robots will enter the safety threshold range within 2 seconds, the system immediately triggers the path planning mechanism. This multi-sensor fusion architecture effectively reduces the probability of false judgments from a single sensor, improves the accuracy of conflict detection through data complementarity, and ensures the safe operation of robots in high-density work environments.

[0044] Furthermore, after acquiring robot position conflict data through the positioning system and lidar sensor, the process includes:

[0045] S301. The acquired multi-source heterogeneous data is time-stamped and synchronized. The multi-source heterogeneous data includes the data acquired by the positioning system and the data acquired by the lidar. The time-stamp synchronization error meets the system's preset synchronization accuracy requirements.

[0046] S302. Use a data fusion algorithm to process the robot position data to eliminate random errors in the positioning system;

[0047] S303. Calculate conflict characteristic parameters such as relative speed and minimum approach distance between adjacent robots based on the processed position data.

[0048] In this embodiment, the system deploys a time synchronization mechanism for heterogeneous devices. Since there is a clock discrepancy between the raw data from the positioning system and the LiDAR, the system calibrates the timestamps using technologies such as Network Time Protocol (NTP) to ensure the consistency of the time reference for multi-source data. The system presets strict synchronization accuracy standards, such as controlling the time error within 10 microseconds, to avoid position calculation errors caused by time discrepancies in high-speed motion scenarios. Furthermore, the system employs a data fusion algorithm to reduce noise in the position data. The UWB positioning system may generate random errors due to multipath effects, and the LiDAR may also experience data offset due to ambient light interference. By using algorithms such as Kalman filtering, combining the long-term positioning stability of UWB with the real-time high-precision characteristics of LiDAR, a data complementary correction model is constructed to achieve dynamic calibration of the positioning data, ultimately outputting high-precision robot pose data. Based on the fused position data, the system constructs a conflict risk assessment model. By calculating characteristic parameters such as the relative speed and minimum approach distance of adjacent robots, a risk quantification index system is established. For example, when the relative speed between two robots reaches 1 m / s and the predicted minimum distance is less than 0.8 meters, the system determines it to be a high-risk conflict state. This model achieves early warning of potential collision risks and autonomous planning of obstacle avoidance paths through a real-time risk assessment mechanism. Its decision-making logic is equivalent to the hazard prediction mechanism in intelligent transportation systems.

[0049] Furthermore, the dynamic path adaptive scheduling mechanism based on the path planning algorithm includes:

[0050] S401. Construct a basic path planning model based on graph search algorithm;

[0051] S402. Introduce time window constraints to construct a multi-robot path conflict avoidance model;

[0052] S403. Optimize and train the path planning model based on machine learning algorithms.

[0053] In this embodiment, graph theory is used to abstract the physical space of the warehouse into a mathematical model. First, a node-edge network graph (G = (V, E)) is created based on the warehouse layout, where: the node set V contains key locations such as shelf locations, aisle intersections, elevator entrances, and charging areas, and each node is labeled with three-dimensional coordinates (x, y, z); the edge set represents the traversable paths between nodes, and each edge is assigned a distance weight and a travel cost. The A* algorithm is used as the core path search algorithm. A heuristic function h(n) is defined to evaluate the distance from node n to the target node, and the total cost f(n) = g(n) + h(n) is calculated by combining the actual cost g(n). For example, the heuristic function uses Euclidean distance to ensure that the estimated distance does not exceed the actual shortest distance; dynamic weight adjustment dynamically adjusts the edge weights based on real-time congestion data during runtime.

[0054] Meanwhile, to address the multi-robot path conflict problem, a time dimension constraint is introduced, and a spatiotemporal path planning model is constructed. A time window is used to allocate a time interval [t] for each node-edge pair (v,e). s ,t e The time window represents the time period that a robot can occupy. If two robots enter adjacent nodes within the same time window, it is considered a potential collision. If the arrival time of a robot at the elevator entrance does not match the elevator's operating cycle, a waiting constraint is triggered. A time-expanded graph is used to embed the time dimension into the basic network graph, and each node is expanded into multiple replicas at different time steps. A resource-constrained project scheduling algorithm is applied, and the optimal time window allocation scheme is solved using the branch and bound method. Here, x(v,e,t) is a binary decision variable, representing whether edge (v,e) is occupied at time t, and t is the passage cost.

[0055] Furthermore, to improve path planning efficiency in dynamic environments, a deep reinforcement learning optimization model is adopted. The state space S includes the robot's position, speed, task priority, remaining battery power, and global path congestion status; the action space A is a discrete set of actions for selecting the next node, such as {A1 = go to node N1, A2 = go to node N2,...}; the reward function R:

[0056] R = w1. Complete the task + w2. Avoid conflicts - w3. Path length - w4. Waiting time

[0057] Where w1 = 100, w2 = 50, w3 = 0.1, and w4 = 0.2 are weighting coefficients.

[0058] Simultaneously, the agent is trained using the Proximal Policy Optimization (PPO) algorithm, with a multilayer perceptron as the policy network. An experience replay buffer is designed to store state-action-reward sequences, improving sample utilization efficiency. A classroom learning mechanism is introduced, gradually transitioning from a simple scenario with 5 robots to a complex scenario with 50 robots. Ten simulations are conducted in the simulation environment. 6 The training process involves 50 time steps per episode; a parameter sharing mechanism is employed to enable the agent to generalize to robot swarms of different sizes; and validation is performed in a real-world environment every 1000 steps, with hyperparameters adjusted based on the validation results.

[0059] Furthermore, the preset backup path set includes:

[0060] S501. Based on the warehouse 3D map, at least three alternative paths with different priorities are pre-generated;

[0061] S502, the path priority is determined by at least two of the following: path length, energy consumption coefficient, equipment usage frequency, and congestion probability;

[0062] S503. When the primary path is occupied or fails, the backup path shall be selected in order of priority.

[0063] In this embodiment, based on a 3D map with a warehouse modeling accuracy of ±5cm, an improved A* algorithm is used to pre-generate at least three backup paths. First, the warehouse space is discretized into a 0.5m × 0.5m × 2m 3D grid, with each grid node containing attributes such as passability and load-bearing limits. Then, using the starting and ending points as endpoints, differentiated paths are generated by adjusting the heuristic function weights and temporary constraints. For example, the first path focuses on the shortest distance, using Euclidean distance as the heuristic function; the second path avoids the frequently used No. 1 freight elevator, forcing the algorithm to select No. 2; the third path restricts areas with aisle widths less than 2m, generating a detour path. At least 30% of the nodes in the three paths do not overlap in spatial orientation, ensuring alternative feasibility in case the main path fails.

[0064] Meanwhile, the priority of backup routes is determined by at least two of the following four indicators: route length, energy consumption coefficient, equipment usage frequency, and congestion probability. Route length is based on the main route length; for every 10% exceeding the main route length, 10 points are deducted, and for less than 10%, 5 points are added. The energy consumption coefficient is calculated based on the robot's acceleration changes and the number of turns along its trajectory; the energy consumption coefficient for smooth paths is set at 1.0, and increases by 0.2 for each sharp turn. Equipment usage frequency is determined by statistically analyzing the usage of freight elevators and aisles involved in the route over the past 24 hours; for equipment exceeding the average by 20%, 15 points are deducted from the corresponding route. Based on historical traffic data and real-time sensor data, the path congestion probability is calculated using a sliding window method; for a probability exceeding 30%, 20 points are deducted. For example, a backup route with a length of 110% of the main route (deduct 10 points), an energy consumption coefficient of 1.2 (deduct 4 points), and a freight elevator usage frequency below the average (add 8 points), has a combined score of -6 points and is ranked second in priority.

[0065] Furthermore, when the system detects path obstruction due to freight elevator malfunction or robot congestion, it automatically switches to a preset backup path according to priority. The detection mechanism includes: determining the elevator's operating status through freight elevator PLC data, and calculating the lane congestion density using LiDAR point cloud data, exceeding 0.5 elevators / m². 2 The system is identified as congested. The switching logic is as follows: First, the highest priority planned path is activated. If new congestion occurs on this path during switching, and the real-time congestion probability is >50%, then a secondary path evaluation is immediately triggered, and the priority is dynamically adjusted. For example, if the route is affected by the failure of freight elevator No. 1, the system prioritizes the use of backup path A, which bypasses freight elevator No. 1. If path A detects that the queuing time for freight elevator No. 2 exceeds 3 minutes during switching, the priority of path A is automatically downgraded, and backup path B, which requires detour but has an idle freight elevator, is activated, ensuring that the path switching response time is controlled within 200ms.

[0066] Furthermore, the edge computing unit executes an alternative path calculation strategy, including:

[0067] S601: Multi-core processors based on edge computing units perform parallel computation of multiple candidate paths;

[0068] S602. Use a heuristic search algorithm to complete the optimal path selection within the system's preset time threshold;

[0069] S603. When computing resources are insufficient, some computing tasks will be offloaded to cloud servers.

[0070] In this embodiment, the edge computing unit uses an Intel Xeon D-2146NT multi-core processor to build a parallel computing architecture, providing hardware acceleration for the alternative path calculation task. Specifically, the warehouse 3D map is divided into multiple computational sub-maps based on aisles and shelving areas, with each sub-map assigned an independent thread for path searching. For example, when three alternative paths are generated, the system simultaneously starts three threads to execute the A* algorithm under different heuristic function configurations: thread 1 uses the shortest distance heuristic, thread 2 uses the lowest energy consumption heuristic, and thread 3 uses the least device dependency heuristic. The multi-core processor's hyper-threading technology allows each physical core to process two threads in parallel, significantly improving computational efficiency and completing the initial generation of three candidate paths within 500ms.

[0071] Meanwhile, to ensure that path calculation is completed within the system's preset time threshold of 500ms, this embodiment employs an improved version of the iterative deepening A* (IDA*) algorithm. The specific strategy is as follows: First, the initial search depth is set to 1.2 times the estimated shortest path length, and a finite search is performed within this depth. If no feasible path is found, the depth is increased by 0.2 times to continue the search until the time threshold is reached. Simultaneously, a pruning strategy is introduced to reduce invalid computation: when the current value of a path exceeds 150% of the optimal value of the found paths, the search for that path is terminated. For example, when searching for the third alternative path, if the system detects that the computation time of a certain branch path has reached 350ms and the value continues to rise, it immediately abandons that branch and switches to other possible paths. Through this mechanism, the system can complete at least 80% of the path evaluation within the time threshold, ensuring the response speed of critical tasks.

[0072] Furthermore, when the edge computing unit load exceeds 70%, the system automatically triggers a computation offloading mechanism. The specific process involves encapsulating computationally complex tasks such as global path optimization and long-term prediction simulation into containerized microservices, which are then transmitted to the cloud server for processing via a 5G network. For example, when more than five robots simultaneously request alternative path calculation strategies, the edge unit offloads secondary tasks, such as the fine-tuning of a third alternative path, to the cloud, while retaining core tasks like the rapid generation of the primary path and the first alternative path. The cloud server uses AWS EC2 P3 instances to accelerate computation, and the processing results are fed back to the edge unit via the MQTT protocol. The entire offloading process has a latency controlled within 150ms. Through this collaborative mechanism, the system can maintain a path calculation success rate of over 95% even under peak load, ensuring the stability of the warehousing and logistics system.

[0073] Furthermore, the control of the warehouse robot to perform the inbound operation according to the planned path includes:

[0074] S701. Generate robot motion control instructions based on path planning results, the instructions including velocity curve, steering angle, and acceleration parameters;

[0075] S702: Transmits control commands to the warehouse robot in real time via an industrial wireless communication network;

[0076] S703. A closed-loop control algorithm is used to control the robot's motion trajectory, and the trajectory tracking error meets the system's preset accuracy requirements.

[0077] In this embodiment, the motion control commands generated based on the path planning results include refined parameters: velocity curve, steering angle, and acceleration parameters. The velocity curve is dynamically adjusted according to the path curvature, with a maximum speed of 1.5 m / s on straight sections and reduced to 0.8 m / s when the turning radius R < 2 m to ensure that the centrifugal force does not exceed 0.3 g. The steering angle is calculated based on the path tangent direction and adopts an S-shaped acceleration and deceleration strategy. When the steering angle > 30°, a pre-deceleration segment is added to ensure smooth steering. The starting acceleration is set to 0.5 m / s². 2 The braking acceleration is set to 0.8 m / s². 2 To prevent cargo from shaking (vibration threshold ≤ 0.5g), the instructions include additional elevator docking parameters for cross-floor operations: slow down to 0.2m / s 3m in advance, and control the positional error within ±5mm when aligning with the elevator door.

[0078] Meanwhile, the system employs a time synchronization protocol to ensure that the clock deviation between command transmission and reception is less than 1μs, avoiding control errors caused by time asynchrony. Control commands are transmitted via an industrial wireless communication network that integrates Wi-Fi 6 and 5G.

[0079] Furthermore, a dual-closed-loop control system is constructed using a PID + feedforward control algorithm. The position loop uses path points as target values, with a sampling period of 10ms, a proportional coefficient Kp = 1.2, an integral coefficient Ki = 0.05, and a derivative coefficient Kd = 0.1, eliminating static errors. The velocity loop uses the velocity curve as target values, with a sampling period of 5ms, Kp = 0.8, Ki = 0.03, and Kd = 0.05, suppressing velocity fluctuations. Feedforward compensation outputs steering torque in advance based on path curvature, compensating for inertial delay and improving dynamic response. The system acquires the robot's position in real time via laser SLAM and compares it with the commanded path. When the trajectory error exceeds ±5cm, a correction mechanism is triggered: error compensation is completed within 500ms, with a corrected acceleration ≤ 0.3m / s². 2 This ensures the stability of the cargo. Actual test data shows that when running at a full load of 500kg, the trajectory error on straight sections is ≤±2cm, and the error on turning sections is ≤±3cm, meeting the system's preset accuracy requirement of ±5cm.

[0080] In this embodiment, as Figure 3 This invention provides an automated goods receiving system for smart warehousing, which implements the aforementioned automated goods receiving method for smart warehousing, comprising:

[0081] The data sensing module is used to acquire elevator malfunction signals and robot position conflict data through sensors;

[0082] The path planning module has a built-in dynamic path adaptive scheduling mechanism and a set of backup paths, wherein the set of backup paths includes at least one backup path.

[0083] The edge computing unit is used to complete the alternative path calculation strategy within a preset time threshold range when a dynamic event is detected.

[0084] The execution control module is used to control the warehouse robot to perform inbound operations according to the planned path;

[0085] The data perception module, path planning module, edge computing unit, and execution control module work together to achieve real-time path optimization under dynamic events.

[0086] In this embodiment, the data perception module serves as the information acquisition hub of the system, constructing a comprehensive monitoring network through multiple types of sensors: vibration, temperature, and current sensors are deployed on key components of the freight elevator to detect abnormal motor operation and door malfunctions in real time; data such as floor position and door opening / closing status are obtained from the freight elevator PLC through industrial communication protocols to achieve digital monitoring of the entire lifecycle of the freight elevator; high-precision positioning technology is used to obtain the three-dimensional coordinates of the warehouse robot in real time, and combined with LiDAR deployed in aisles and intersections, the surrounding environment is continuously scanned to identify the robot's position, movement trajectory, and potential collision risks; infrared sensors, proximity switches, and other devices are used to monitor the relative distance between the robot and shelves and obstacles to ensure operational safety. Simultaneously, the module performs multi-layered processing on the collected raw data, transforming it into effective event information: filtering algorithms are used to eliminate noise interference in sensor data; timestamp synchronization technology ensures the time consistency of multi-source data, avoiding misjudgments due to time deviations; key features are extracted from vibration, current, and other data, and machine learning algorithms are combined to build an equipment health model, identifying fault characteristics such as abnormal elevator vibration and current overload; based on robot motion trajectory and speed data, collision prediction algorithms are used to determine potential conflict risks; detected problems are classified according to their degree of abnormality (e.g., minor abnormalities, emergency faults), and when a preset threshold is reached, dynamic event information (e.g., "elevator motor abnormality" or "robot path conflict") is immediately generated and pushed to the system's core processing unit. Through this design, the data perception module can quickly and accurately capture dynamic changes in the warehouse environment, providing a reliable information foundation for subsequent path planning and task scheduling, effectively improving the system's intelligent decision-making capabilities and emergency response efficiency.

[0087] Meanwhile, the path planning module, as the core decision-making unit of the system, integrates dynamic path planning and backup path management functions: based on the warehouse 3D map, it uses graph search algorithms to pre-generate multiple basic paths from the warehouse entrance to each target shelf and stores them in the path database. Each path includes parameters such as node sequence, turning point coordinates, and estimated travel time, providing basic data support for subsequent planning; it has a built-in time window constraint and priority evaluation mechanism. When the data perception module detects dynamic events such as elevator malfunctions or robot conflicts, the system immediately triggers path planning. By comprehensively evaluating factors such as path length, equipment usage frequency, and congestion probability, it assigns priority to each backup path and automatically selects the optimal path according to the priority order; combined with historical operating data and real-time status feedback, the path planning module can dynamically adjust path weights and priority strategies. For example, if a path frequently experiences congestion, its priority is reduced; if a backup path performs well in actual use, its weight is increased, achieving continuous optimization of the path planning strategy.

[0088] Furthermore, the edge computing unit, as the real-time computing core of the system, undertakes the functions of rapid path decision-making and task offloading: based on a multi-core processor architecture, it performs parallel computing on multiple candidate paths, significantly shortening the path planning time. Through optimization algorithms and hardware collaboration, it ensures that the calculation and selection of backup paths are completed within the system's preset time threshold, meeting the stringent real-time requirements of the warehousing scenario. When computing resources are sufficient, the edge computing unit independently completes the path planning task. If the load is too high, it automatically offloads non-urgent computing tasks (such as fine-tuning of paths) to the cloud server, realizing collaborative processing between the edge and the cloud, and avoiding response delays due to insufficient resources. The calculation results of frequently used paths are cached in local storage. When the same type of task is requested again, the cached data is directly called to reduce redundant calculations, further improving the system response speed and ensuring that the robot can quickly obtain new path instructions under dynamic events.

[0089] Furthermore, the execution control module, as the system's terminal execution unit, is responsible for transforming the path planning results into the robot's actual actions. Based on information such as the curvature and distance of the planned path, it generates motion control commands including speed curves, steering angles, and acceleration parameters. For example, it sets a maximum safe speed on straight sections and automatically reduces speed and adjusts the steering angle in turning areas to ensure smooth robot operation. The control commands are transmitted to the warehouse robot in real time via industrial wireless communication networks (such as 5G and Wi-Fi 6), and a two-way communication mechanism is established to receive real-time feedback on the robot's position, speed, and other statuses, enabling dynamic monitoring of the robot's operating status. A PID closed-loop control algorithm is used to compare the robot's actual motion trajectory with the planned path in real time. When the trajectory error exceeds a preset range, the control parameters are automatically adjusted to ensure the robot strictly follows the planned path for warehousing operations. Ultimately, the trajectory tracking error meets the system's accuracy requirements, ensuring the accuracy and safety of goods transportation.

[0090] In summary, this invention constructs a complete and efficient intelligent warehousing automated goods receiving system through the collaborative operation of a data perception module, a path planning module, an edge computing unit, and an execution control module. The data perception module, as the system's front-end information acquisition unit, can accurately capture dynamic events such as elevator malfunctions and robot path conflicts; the path planning module, based on a multi-dimensional evaluation system, presets backup paths and implements dynamic optimization, forming the system's decision-making core; the edge computing unit, through localized parallel computing, ensures that path planning is completed within the system's preset time threshold, achieving rapid response; and the execution control module, through high-precision motion control algorithms, ensures that the warehousing robot accurately executes operations according to the planned path. This system overcomes the technical bottlenecks of frequent manual intervention and delayed response in traditional warehousing, achieving full-process automation and intelligence from anomaly detection and path planning to task execution. Practical verification has shown that this system can significantly improve warehousing and logistics operating efficiency, reduce equipment failure rates, and provide an innovative technical solution for the efficient and stable operation of modern intelligent warehousing.

[0091] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for automated goods receiving in intelligent warehousing, the automated receiving method being applied to an automated receiving system, the automated receiving system being used to control warehouse robots for receiving goods, and comprising multiple edge computing units, characterized in that, The automated data entry method includes: Obtain dynamic event information in the goods receiving scenario through a real-time data interface; When a dynamic event is detected, at least one edge computing unit is selected to jointly generate at least one backup path for the warehouse robot based on the dynamic event information, the location information of the warehouse robot, and the surrounding image information collected by the warehouse robot. The processing time of the selected edge computing units is shorter than the system preset time. Control the warehouse robot to perform the inbound operation according to one of the alternative routes; The real-time data interface includes an industrial communication protocol interface for data interaction with the elevator controller and the robot controller. The dynamic event information includes elevator fault signals and robot position conflict detection data. A dynamic path adaptive scheduling mechanism is constructed based on path planning algorithms, and a set of backup paths is preset, including: A basic path planning model is constructed based on a graph search algorithm; A multi-robot path conflict avoidance model is constructed by introducing time window constraints. The path planning model is optimized and trained based on machine learning algorithms; At least three alternative routes with different priorities are pre-generated based on the warehouse 3D map; The path priority is determined by a combination of at least two of the following: path length, energy consumption coefficient, equipment usage frequency, and congestion probability. When the primary path is occupied or fails, the backup path is selected in order of priority.

2. The method according to claim 1, characterized in that, The freight elevator fault signal further includes: The operating status data of the freight elevator PLC is obtained through the industrial Ethernet interface. The operating status data includes the freight elevator position, load weight, and door open / close status.

3. The method according to claim 1, characterized in that, The robot position conflict detection data further includes: The positioning system acquires the three-dimensional coordinate data of each warehouse robot in real time, and the positioning accuracy of the positioning system meets the system's preset positioning error requirements. Robot positional conflict data is acquired by deploying lidar sensors, and the scanning frequency and angular resolution of the lidar meet the system's preset detection accuracy requirements.

4. The method according to claim 3, characterized in that, After acquiring robot position conflict data through the positioning system and lidar sensor, the process further includes: The acquired multi-source heterogeneous data is time-stamped and synchronized. The multi-source heterogeneous data includes data acquired by the positioning system and data acquired by the lidar. The time-stamp synchronization error meets the system's preset synchronization accuracy requirements. A data fusion algorithm is used to process the robot's position data to eliminate random errors in the positioning system; Based on the processed position data, conflict characteristic parameters such as the relative speed and minimum approach distance of adjacent robots are calculated.

5. The method according to claim 1, characterized in that, The edge computing unit executes an alternative path calculation strategy, further including: Multiple candidate paths are computed in parallel using a multi-core processor based on edge computing units; The optimal path is selected within the system's preset time threshold using a heuristic search algorithm. When computing resources are insufficient, some computing tasks are offloaded to cloud servers.

6. The method according to claim 1, characterized in that, The control of the warehouse robot to perform the inbound operation according to the planned path further includes: Based on the path planning results, robot motion control commands are generated, including velocity curves, steering angles, and acceleration parameters. Control commands are transmitted to the warehouse robot in real time via an industrial wireless communication network; A closed-loop control algorithm is used to control the robot's motion trajectory, and the trajectory tracking error meets the system's preset accuracy requirements.

7. An automated goods receiving system for intelligent warehousing, used to implement the automated goods receiving method for intelligent warehousing as described in any one of claims 1-6, characterized in that, include: The data sensing module is used to acquire elevator malfunction signals and robot position conflict data through sensors; The path planning module has a built-in dynamic path adaptive scheduling mechanism and a set of backup paths, wherein the set of backup paths includes at least one backup path. The edge computing unit is used to complete the alternative path calculation strategy within a preset time threshold range when a dynamic event is detected. The execution control module is used to control the warehouse robot to perform inbound operations according to the planned path; The data perception module, path planning module, edge computing unit, and execution control module work together to achieve real-time path optimization under dynamic events.

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