A small intelligent central storage system

Abstract

The present invention relates to the technical field of intelligent warehousing systems, and discloses a small intelligent central storage system. It includes a central three-dimensional warehouse management module, a bin robot control module, a warehousing management module, an AGV scheduling module, and a slide table control module. The central three-dimensional warehouse management module generates allocation instructions based on material attributes and preset rules; the bin robot control module receives the instructions and controls the robot to carry bins through a dynamic obstacle avoidance algorithm; the warehousing management module generates inbound and outbound instructions and associates material information; the AGV scheduling module uses a topological map and an ant colony algorithm to plan the AGV transportation path; the slide table control module responds to operation signals, controls the slide table, and triggers positioning signals. This system can accurately store and allocate, efficiently handle materials, intelligently schedule AGVs, and achieve accurate slide table positioning, effectively improving the efficiency of warehousing management, reducing costs, and ensuring the safety of goods.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent warehousing systems, and particularly to a small intelligent central storage system. Background Art

[0002] An intelligent central storage system refers to a material storage and handling system integrating equipment such as a central three-dimensional warehouse, bin robots, an automated warehousing management system, and an automatic guided vehicle (AGV). In the fields of modern production manufacturing and warehousing management, traditional storage systems are facing numerous challenges, which seriously restrict the operation efficiency and competitiveness of enterprises. The traditional storage system lacks scientificity in the layout of material storage. In most cases, the storage locations of materials are only based on manual experience or simple classification, without fully considering key attributes such as the access frequency, volume, and weight of materials. For example, some materials with high access frequencies are placed deep in the warehouse or on high shelves, and each retrieval requires a large amount of time for the scheduling and transportation of handling equipment, which not only reduces production efficiency but also increases labor costs. At the same time, due to the lack of accurate calculation of the shelf load-bearing capacity and volume, there are often phenomena of shelf overload or space waste, shortening the service life of the shelves and failing to maximize the use of warehousing space.

[0003] In the material handling link, the traditional system relies on handling equipment with fixed paths or manual guidance and lacks the ability of dynamic obstacle avoidance. The warehouse environment is complex and changeable, the stacking positions of goods are not fixed, and personnel move frequently. Handling equipment is extremely likely to encounter obstacles during operation. Once encountering an obstacle, traditional equipment often can only stop running and wait for manual intervention, or operate according to a preset simple avoidance strategy, which not only affects the handling efficiency but also may cause equipment collision and damage, increasing maintenance costs and safety risks.

[0004] In terms of warehousing management, the traditional system has a low level of informatization, and the association between material information and bins is not close or timely. The inbound and outbound information of materials relies on manual records and checks, which are prone to errors and omissions, resulting in inaccurate inventory data. When there are changes in material requirements on the production line, it is impossible to quickly and accurately locate and allocate the required materials, affecting the production progress. Moreover, in the face of urgent production tasks, due to the lack of effective management of material priorities, it is difficult to respond quickly and ensure the supply of key materials.

[0005] In terms of transportation route planning, the traditional AGV scheduling method is inefficient. Usually, it travels along a preset fixed route and cannot be dynamically adjusted according to real-time traffic conditions and task requirements. When the warehouse traffic is busy, it is easy to cause AGV congestion, resulting in delays in material transportation and further affecting the operation efficiency of the entire warehousing logistics.

[0006] With the increasingly fierce market competition, enterprises' demand for the intelligence and high efficiency of warehousing and logistics systems is becoming more and more urgent. The existing storage systems can no longer meet the requirements of modern enterprises for quickly responding to market changes, reducing costs, and improving production efficiency. Therefore, it is of great practical significance to develop a small intelligent central storage system that can scientifically allocate storage locations, has a dynamic obstacle avoidance function, realizes intelligent warehousing management, and efficient transportation route planning. Summary of the Invention

[0007] The purpose of the present invention is to provide a small intelligent central storage system to solve the problems raised in the above background technology.

[0008] To achieve the above purpose, the present invention provides the following technical solutions: A small intelligent central storage system, the system includes:

[0009] A central three-dimensional warehouse management module, which is used to generate a storage allocation instruction for the three-dimensional warehouse according to the material attributes and preset storage rules, and the storage allocation instruction includes the level and position information of the material in the shelf;

[0010] A bin robot control module, which is used to receive the storage allocation instruction and parse the shelf coordinates, and control the bin robot to carry the bin to the target shelf along the preset path, and the preset path is updated in real time based on the dynamic obstacle avoidance algorithm;

[0011] A warehousing management module, which is used to generate a material outbound instruction or an inbound instruction according to the production line scheduling requirements, and associate the bin number with the material information, and the material information includes the material type, quantity, and priority;

[0012] An AGV scheduling module, which is used to match idle AGVs according to the material outbound instruction, plan the optimal transportation path, and send the path coordinates to the AGV execution end, and the optimal transportation path is determined by the fusion of the topological map and the ant colony algorithm;

[0013] A slide control module, which is used to respond to the manual operation signal, control the slide to move to the specified work station, and trigger a bin positioning signal after the material loading is completed, and the positioning signal includes the docking coordinates between the slide and the shelf.

[0014] Preferably, the generation of the storage allocation instruction by the central three-dimensional warehouse management module includes:

[0015] Obtain the volume, weight, and access frequency parameters of the material;

[0016] Divide the shelf levels according to the access frequency parameters, and allocate the materials with high access frequency to the middle-layer shelves, and allocate the materials with low access frequency to the top or bottom shelves;

[0017] Calculate the remaining load capacity of the shelf according to the volume and weight parameters, and select the shelf position with the satisfied remaining capacity as the target storage position.

[0018] Preferably, the dynamic obstacle avoidance algorithm in the bin robot control module includes:

[0019] Collect real-time environmental point cloud data in the traveling direction of the bin robot;

[0020] Identify the contour and movement trend of obstacles in the point cloud data, and generate a predicted trajectory of the obstacles;

[0021] Calculate the speed adjustment parameter and path offset of the bin robot based on the predicted trajectory, and generate an obstacle avoidance path.

[0022] Preferably, the warehousing management module associates bin numbers with material information, including:

[0023] Assign a unique number to each bin, and bind the material type and quantity in the warehousing instruction;

[0024] Assign a priority label to the material according to the processing sequence of the production line, and the priority label is used to trigger an emergency outbound instruction;

[0025] Generate a bin status list, and update the bin position, loading status and the associated instruction number in real time.

[0026] Preferably, the optimal transportation path is determined by fusing a topological map with an ant colony algorithm, including:

[0027] Construct topological map nodes of the warehousing area, and mark the passing distance and turning weight between the nodes;

[0028] Based on the ant colony algorithm, simulate the path optimization process of multiple AGVs, and select the path with the highest pheromone concentration as the initial path;

[0029] Update the topological map node weights according to the real-time AGV position, and dynamically adjust the path pheromone distribution.

[0030] Preferably, the slide control module triggers the bin positioning signal, including:

[0031] Receive the material loading completion confirmation signal sent by the manual operation terminal;

[0032] Detect the relative position deviation between the slide and the shelf through a laser sensor;

[0033] Calculate the deviation compensation value and generate the docking coordinates, and send them to the bin robot control module.

[0034] Preferably, the calculation of the remaining load capacity of the shelf includes:

[0035] Obtain the cumulative weight and volume of the materials currently stored on the target shelf;

[0036] Calculate the remaining load-bearing capacity and remaining volume according to the maximum load-bearing capacity and volume limit of the shelf;

[0037] Screen the shelf positions where the remaining load-bearing capacity is greater than the weight of the material to be stored and the remaining volume is greater than the volume of the material.

[0038] Preferably, the generation of the obstacle prediction trajectory includes:

[0039] Extract the movement direction and velocity vector of the obstacle in the point cloud data;

[0040] Predict the position change of the obstacle within the next three seconds based on the Kalman filtering algorithm;

[0041] Map the predicted position to the path coordinate system of the bin robot to generate a trajectory conflict area.

[0042] Preferably, the triggering of the emergency outbound instruction includes:

[0043] Monitor the processing progress of the production line and identify the material shortage signal;

[0044] Retrieve the bin status list and lock the bin number that contains the shortage material and has the highest priority label;

[0045] Send a forced interruption instruction to the bin robot control module to give priority to the outbound operation of this bin.

[0046] Preferably, the dynamic adjustment of the path pheromone distribution includes:

[0047] Update the path pheromone attenuation coefficient according to the deviation between the actual passing time and the estimated time of the AGV;

[0048] Reduce the pheromone concentration of the congested path nodes and increase the pheromone concentration of the idle path nodes;

[0049] Recalculate the AGV path selection probability based on the updated pheromone concentration.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] The small intelligent central storage system of the present invention has many remarkable beneficial effects. In the storage allocation link, the central three-dimensional warehouse management module performs storage allocation based on the attributes of materials such as volume, weight, and access frequency, in combination with preset storage rules. By obtaining various parameters of the materials, materials with high access frequency are allocated to the middle-layer shelves, and materials with low access frequency are allocated to the top or bottom shelves, which can greatly reduce the material access time. For example, in an electronic product manufacturing enterprise, commonly used electronic components have a high access frequency and are placed on the middle-layer shelves. Workers or equipment do not need to climb up and down the high-layer shelves frequently, saving a large amount of picking time and improving production efficiency. At the same time, by calculating the remaining load capacity of the shelves based on the volume and weight parameters and reasonably selecting the target storage location, the force on the shelves can be evenly distributed, the service life of the shelves can be extended, and the enterprise's equipment maintenance cost can be reduced.

[0052] The bin robot control module uses a dynamic obstacle avoidance algorithm to collect environmental point cloud data in real time, identify the obstacle contour and movement trend, and generate a predicted trajectory. Based on this, it calculates the speed adjustment parameters and path offset to generate an obstacle avoidance path. This enables the bin robot to flexibly avoid obstacles in a complex warehousing environment, avoiding damage to goods caused by collisions and equipment failures.

[0053] The warehousing management module assigns a unique number to each bin and associates information such as material type, quantity, and priority. In this way, the storage and flow of materials can be accurately grasped. During production line scheduling, an emergency outbound order is triggered according to the material priority label to ensure that the production line can quickly obtain the required materials when there is a shortage of materials, avoiding production interruptions.

[0054] The AGV scheduling module determines the optimal transportation path through the fusion of a topological map and the ant colony algorithm. It constructs the topological map nodes of the warehousing area, marks relevant information, simulates the path optimization process of multiple AGVs, selects the path with the highest pheromone concentration as the initial path, and dynamically adjusts the pheromone distribution of the path according to the real-time position of the AGV. This method can effectively reduce congestion and conflicts between AGVs and improve transportation efficiency.

[0055] The slide control module responds to the manual operation signal, controls the slide to move to the specified station, and detects the relative position deviation through a laser sensor after the material loading is completed, calculates the deviation compensation value, and generates the docking coordinates. This greatly improves the docking accuracy between the bin robot and the slide, reduces the docking adjustment time, and improves the overall handling efficiency.

[0056] Overall, through the collaborative work of each module, the small intelligent central storage system of the present invention effectively solves many problems existing in traditional warehousing systems, realizes the intelligentization and high efficiency of warehousing management, plays an important role in improving production efficiency, reducing costs, and ensuring the safety of goods, has significant economic and social benefits, and provides strong technical support for the warehousing management upgrade of related industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 FIG. is the working principle diagram of the small intelligent central storage system of the present invention;

[0058] Figure 2 FIG. is the schematic diagram of the principle of associating the bin number with the material information by the warehousing management module;

[0059] Figure 3 FIG. is the schematic diagram of the principle of calculating the remaining load capacity of the shelf;

[0060] Figure 4 FIG. is the schematic diagram of the principle of generating the predicted trajectory of the obstacle. DETAILED DESCRIPTION OF THE INVENTION

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] Please refer to Figures 1-4 , the present invention provides a small intelligent central storage system, which mainly consists of a central stereoscopic warehouse management module, a bin robot control module, a warehousing management module, an AGV scheduling module, and a sliding table control module.

[0063] Central stereoscopic warehouse management module: This module generates a storage allocation instruction for the stereoscopic warehouse according to the material attributes and preset storage rules. The material attributes cover key information such as volume, weight, and access frequency. After obtaining these attributes, the module processes them according to the preset rules. For example, according to the access frequency, the high-frequency access materials are allocated to the middle-layer shelves, and the low-frequency access materials are allocated to the top or bottom shelves; then, according to the volume and weight, the remaining load capacity of the shelf is calculated to determine the level and position information of the material on the shelf, and finally, a storage allocation instruction is generated.

[0064] Bin robot control module: Receives the storage allocation instruction generated by the central stereoscopic warehouse management module and parses the shelf coordinates therein. Through a preset dynamic obstacle avoidance algorithm, the bin robot can real-time sense the environmental information in the traveling direction, avoid obstacles, and transport the bin to the target shelf along the continuously updated preset path.

[0065] Warehousing management module: Generates a material outbound instruction or an inbound instruction according to the production line scheduling requirements. During the instruction generation process, the bin number is associated with the material information, and the material information includes material type, quantity, and priority. The warehousing management module also monitors and updates the bin status in real time to ensure that the system always grasps the material storage situation.

[0066] AGV Scheduling Module: After receiving the instruction for material outbound, this module matches idle AGVs from numerous ones and plans the optimal transportation path by integrating the topological map and ant colony algorithm. After the planning is completed, it sends the path coordinates to the AGV execution end, enabling the AGV to efficiently transport materials.

[0067] Sliding Table Control Module: After receiving the manual operation signal, it controls the sliding table to move to the designated workstation. When the material loading is completed, the sliding table control module detects relevant information through devices such as laser sensors, triggers the signal for bin positioning, and sends out the docking coordinates between the sliding table and the shelf for the subsequent operations to proceed smoothly.

[0068] Through the close cooperation of the above-mentioned modules, the small intelligent central storage system realizes the intelligent storage, handling, and transportation of materials, improving the efficiency and accuracy of warehouse management.

[0069] Next, the technical solutions of the present invention will be further elaborated through the following 5 embodiments.

[0070] Embodiment 1:

[0071] This embodiment focuses on the specific process of the central stereoscopic warehouse management module generating storage allocation instructions. In the actual warehouse management scenario, there are various types of materials, and their property differences significantly affect the storage efficiency. The central stereoscopic warehouse management module first obtains the volume , weight and access frequency parameters of the materials. There are various ways to obtain these parameters. For example, when the materials are put into storage, the volume and weight of the materials are measured by sensors, and at the same time, the access frequency is statistically calculated based on the past usage records.

[0072] According to the access frequency parameter , the shelf levels are divided. A threshold for access frequency is set . When , it is determined that the material is a high-frequency access material and is allocated to the middle layer of the shelf, which can reduce the transportation time during frequent access operations and improve the overall efficiency; when , the material is a low-frequency access material and is allocated to the top or bottom layer of the shelf.

[0073] Then, the remaining load-bearing capacity of the shelf is calculated according to the volume and weight parameters. The cumulative weight and volume of the materials currently stored on the target shelf are obtained. According to the maximum load-bearing capacity and volume limit of the shelf, the remaining load-bearing capacity and the remaining volume . Screen the remaining load-bearing capacity Greater than the weight of the material to be stored And the remaining volume Greater than the volume of the material The shelf position that meets the above conditions is used as the target storage position. For example, a certain shelf has a maximum load-bearing capacity of 500 kg, the cumulative weight of the materials already stored is 300 kg, the maximum volume is 10 cubic meters, the volume of the materials already stored is 6 cubic meters, there is a material with a weight of 100 kg and a volume of 2 cubic meters. After calculation, the remaining load-bearing capacity of this shelf is 200 kg, which is greater than the material weight of 100 kg, and the remaining volume is 4 cubic meters, which is greater than the material volume of 2 cubic meters. This shelf position can be used as the target storage position for this material. In this way, both the safe load-bearing of the shelf is ensured and the shelf space is fully utilized, realizing the reasonable storage allocation of materials.

[0074] Embodiment 2:

[0075] This embodiment details the implementation process of the dynamic obstacle avoidance algorithm in the control module of the bin robot. During the operation of the bin robot, the surrounding environment is complex and changeable, and the dynamic obstacle avoidance algorithm is the key to its safe and efficient operation.

[0076] The bin robot collects the environmental point cloud data in the traveling direction in real time through devices such as lidar carried on it. These point cloud data contain the position information of various objects in the robot's surrounding environment.

[0077] Identify the contour and motion trend of obstacles in the point cloud data. Using advanced image processing algorithms, the collected point cloud data is processed to extract the boundary points of the obstacles, and then the contour of the obstacles is fitted. At the same time, by analyzing the changes in the positions of obstacles in the point cloud data at different times, the movement direction and speed are determined, and the predicted trajectory of the obstacles is generated. Specifically, extract the movement direction vector of the obstacles in the point cloud data And the speed vector . Based on the Kalman filter algorithm, predict the position changes of obstacles within the next three seconds. The Kalman filter algorithm is an optimal linear estimation method. By fusing the prediction of the system state and the observed data, the estimation of the obstacle position is continuously optimized. Let the position of the obstacle at the current moment be , According to the movement direction vector And the speed vector , Predict the position of the obstacle within the next three seconds ( Is time, taking values of 1, 2, and 3 seconds). Map the predicted position to the path coordinate system of the bin robot to generate a trajectory conflict area. For example, if it is predicted that the obstacle will move onto the preset path of the bin robot within the next three seconds, then this area is the trajectory conflict area.

[0078] Based on the predicted trajectory, the speed adjustment parameters and path offset of the container robot are calculated to generate an obstacle avoidance path. When the trajectory conflict area is detected, the speed adjustment parameters are calculated based on the movement state of the obstacle and the performance of the robot itself. and path offset For example, if the obstacle is fast and close, the robot speed may need to be significantly reduced and the path adjusted. By continuously adjusting the speed and path, the box robot can safely avoid obstacles and continue to move toward the target shelf along the newly generated obstacle avoidance path, ensuring the safety and reliability of the material handling process.

[0079] Embodiment 3:

[0080] This embodiment focuses on the specific way in which the warehouse management module associates the bin number with the material information. The warehouse management module plays the role of an information hub in the entire storage system, and accurately associating the bin number with the material information is crucial for efficient material management.

[0081] Assign a unique number to each container , bind the material type in the warehousing instruction and quantity For example, in the storage system of an electronic component manufacturer, the material type bound to the material box numbered 001 when entering the warehouse is resistor, and the quantity is 1,000. Through this one-to-one correspondence, the system can quickly locate and manage the materials in each material box.

[0082] Assign priority labels to materials based on the production line processing sequence The order of production line processing may vary depending on factors such as the urgency of the production task and customer order requirements. For example, during the production of a batch of urgent orders, certain key materials need to be supplied first. At this time, the priority labels of these materials are Set to a higher value. Priority Label Used to trigger emergency outbound instructions.

[0083] Generate a bin status list and update bin locations in real time , Loading status And the instruction number . Bin location The loading status is obtained by positioning devices installed on the boxes and shelves. The sensor can be used to detect whether the material box is full of materials to determine the instruction number The system will update the status list of the material box in time and record the loading status of the material box. Updated to "Out of stock", the instruction number Update it to the corresponding outbound order number so that the system can always grasp the dynamic information of the bin and provide an accurate basis for subsequent warehousing management decisions.

[0084] Example 4:

[0085] This example details the process of determining the optimal transportation path through the fusion of a topological map and the ant colony algorithm. In a warehousing environment, the efficient transportation of AGVs depends on reasonable path planning, and the fusion of a topological map and the ant colony algorithm provides a powerful means for achieving optimal path planning.

[0086] Construct the topological map nodes of the warehousing area. Define the key positions in the warehousing area, such as shelf positions, aisle intersections, loading and unloading points, etc. as topological map nodes. Mark the passing distances and turning weights , where and represent different nodes respectively. The passing distance can be obtained through actual measurement or calculated according to the map scale, and the turning weight reflects the difficulty of turning from node to node . For example, a right-angle turn may have a higher turning weight than a smooth turn.

[0087] Simulate the multi-AGV path optimization process based on the ant colony algorithm. The ant colony algorithm is a heuristic algorithm that simulates the foraging behavior of ants. At the beginning of the algorithm, assume that each AGV starts from the starting node and selects the next node according to the pheromone concentration and heuristic information on the path. The heuristic information is usually determined according to the distance between nodes. The shorter the distance, the greater. The selection probability is calculated by the formula , where and are parameters that control the relative importance of pheromone concentration and heuristic information, represents the set of nodes that the current AGV can choose. In the initial stage, the pheromone concentrations on all paths are the same. As the AGV continuously moves on the path, the pheromone will gradually accumulate and volatilize. Select the path with the highest pheromone concentration as the initial path.

[0088] Update the weights of the topological map nodes according to the real-time position of the AGV. When the AGV is traveling on the path, the system will obtain its position information in real time. If there are a large number of AGVs on a certain path, resulting in congestion, it indicates that the traffic efficiency of this path is low. At this time, reduce the pheromone concentration of the nodes on this path, and at the same time increase the pheromone concentration of the idle path nodes. By continuously adjusting the pheromone concentration, the AGV will gradually tend to choose a better path, realizing the dynamic optimization of the path. Recalculate the AGV path selection probability based on the updated pheromone concentration, so that the AGV can select the optimal path according to the real-time road conditions and improve the transportation efficiency.

[0089] Embodiment 5:

[0090] This embodiment details the specific operation of the sliding table control module to trigger the bin positioning signal and the triggering process of the related emergency outbound instruction. The sliding table control module plays an important role in the connection link of material handling and transportation, and the effective triggering of the emergency outbound instruction ensures the normal operation of the production line.

[0091] The sliding table control module receives the material loading completion confirmation signal sent by the manual operation terminal. When the operator completes the material loading at the designated workstation, a confirmation signal is sent to the sliding table control module through the operation terminal to inform the system that the material loading has been completed and the next operation can be carried out.

[0092] Detect the relative position deviation between the sliding table and the shelf through a laser sensor. The laser sensor emits a laser beam, measures the time from the emission to the reflection of the laser beam, and calculates the distance between the sliding table and the shelf according to the speed of light. Let the distance between the sliding table and the shelf in the horizontal direction be and the distance in the vertical direction be . By measuring the distance data of multiple points, the relative position deviation between the sliding table and the shelf can be calculated.

[0093] Calculate the deviation compensation value and generate the docking coordinates. According to the measured relative position deviation, calculate the deviation compensation values and . The calculation formula for the docking coordinates is , . Send the docking coordinates to the bin robot control module, and the bin robot accurately transports the bin to the sliding table for docking according to these coordinates to ensure the accuracy of material transportation.

[0094] In terms of triggering the emergency outbound instruction, the system monitors the production progress of the production line and identifies the material shortage signal. Through real-time communication with the production line control system, obtain the material usage situation of each workstation on the production line. When the material quantity at a certain workstation is lower than the set threshold, the system determines that there is a material shortage and generates a material shortage signal.

[0095] Retrieve the bin status list and lock the bin number that contains the shortage materials and has the highest priority label. The system searches for the bins containing the shortage materials in the bin status list based on the material shortage signal and determines the bin number with the highest priority according to the priority label Determine the bin number with the highest priority.

[0096] Send a forced interruption instruction to the bin robot control module to give priority to the outbound operation of this bin. After receiving the forced interruption instruction, the bin robot control module immediately stops the current task (if performing other operations), and gives priority to transporting the locked bin from the shelf to the outbound position to ensure that the production line can obtain the required materials in a timely manner, avoid production line stagnation caused by material shortages, and ensure the smooth progress of production activities.

[0097] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0098] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A small intelligent central storage system, characterized in that, The system includes: A central automated storage and retrieval system (AS / RS) management module, which is used to generate storage allocation instructions for the AS / RS according to material attributes and preset storage rules. The storage allocation instructions include the level and location information of the materials on the shelves. A bin robot control module, which is used to receive the storage allocation instructions, parse the shelf coordinates, and control the bin robot to transport the bin to the target shelf along a preset path. The preset path is updated in real time based on a dynamic obstacle avoidance algorithm. A warehousing management module, which is used to generate material outbound instructions or inbound instructions according to the production line scheduling requirements, and associate the bin number with the material information. The material information includes material type, quantity, and priority. An AGV scheduling module, which is used to match idle AGVs according to the material outbound instructions, plan the optimal transportation path, and send the path coordinates to the AGV execution end. The optimal transportation path is determined by the fusion of a topological map and an ant colony algorithm. A slide table control module, which is used to respond to the manual operation signal, control the slide table to move to the specified workstation, and trigger a bin positioning signal after the material loading is completed. The positioning signal includes the docking coordinates between the slide table and the shelf. The generation of the storage allocation instructions by the central AS / RS management module includes: Obtaining the volume, weight, and access frequency parameters of the materials. Dividing the shelf levels according to the access frequency parameters, and allocating the materials with high access frequency to the middle shelves, and the materials with low access frequency to the top or bottom shelves. Calculating the remaining load capacity of the shelves according to the volume and weight parameters, and selecting the shelf positions with sufficient remaining capacity as the target storage positions. The dynamic obstacle avoidance algorithm in the bin robot control module includes: Real-time collecting the environmental point cloud data in the traveling direction of the bin robot. Identifying the contour and movement trend of the obstacles in the point cloud data, and generating the predicted trajectory of the obstacles. Calculating the speed adjustment parameters and path offset of the bin robot based on the predicted trajectory, and generating an obstacle avoidance path. The association of the bin number with the material information by the warehousing management module includes: Assigning a unique number to each bin, and binding the material type and quantity in the inbound instruction. Allocating priority labels to the materials according to the production line processing sequence. The priority labels are used to trigger emergency outbound instructions. Generating a bin status list, and real-time updating the bin position, loading status, and the associated instruction number. The triggering of the bin positioning signal by the slide table control module includes: Receiving the material loading completion confirmation signal sent by the manual operation terminal. Detecting the relative position deviation between the slide table and the shelf through a laser sensor. Calculating the deviation compensation value and generating the docking coordinates, and sending them to the bin robot control module. The generation of the predicted trajectory of the obstacles includes: Extracting the movement direction and speed vector of the obstacles in the point cloud data. Predicting the position change of the obstacles within the next three seconds based on the Kalman filter algorithm. Mapping the predicted positions to the path coordinate system of the bin robot to generate a trajectory conflict area.

2. The small intelligent central storage system according to claim 1, characterized in that, The optimal transportation path is determined by the fusion of a topological map and an ant colony algorithm, including: Constructing the topological map nodes of the warehousing area, and marking the passing distance and turning weight between the nodes. Based on the ant colony algorithm, simulating the path optimization process of multiple AGVs, and selecting the path with the highest pheromone concentration as the initial path. Updating the weights of the topological map nodes according to the real-time AGV positions, and dynamically adjusting the pheromone distribution of the path.

3. The small intelligent central storage system according to claim 1, characterized in that, The calculation of the remaining load-bearing capacity of the shelf includes: Obtain the cumulative weight and volume of the materials currently stored on the target shelf; Calculate the remaining load-bearing capacity and remaining volume according to the maximum load-bearing capacity and volume limit of the shelf; Filter out the shelf positions where the remaining load-bearing capacity is greater than the weight of the materials to be stored and the remaining volume is greater than the volume of the materials.

4. The small intelligent central storage system according to claim 1, wherein The triggering of the emergency outbound order includes: Monitor the processing progress of the production line and identify the material shortage signal; Retrieve the list of bin status, and lock the bin number that contains the shortage materials and has the highest priority label; Send a forced interruption instruction to the bin robot control module to give priority to the outbound operation of this bin.

5. The small intelligent central storage system according to claim 2, wherein The dynamic adjustment of the pheromone distribution of the path includes: Update the pheromone attenuation coefficient according to the deviation between the actual passing time and the estimated time of the AGV; Reduce the pheromone concentration at the congested path nodes and increase the pheromone concentration at the idle path nodes; Recalculate the AGV path selection probability based on the updated pheromone concentration.

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