Multi-layer three-dimensional warehouse automatic sowing path optimization scheduling method and system

Abstract

The invention relates to the field of three-dimensional warehouse automatic sowing, in particular to a multi-layer three-dimensional warehouse automatic sowing path optimization scheduling method and system, and the method comprises the following steps: obtaining sowing association data and three-dimensional warehouse association data through a data interaction module, and constructing a multi-dimensional mapping table; calling a preset material characteristic and path constraint mapping library, screening paths conforming to constraints through an improved A algorithm, calculating path cost, and outputting an initial path; the real-time load state of the AGV is input, a regional task allocation plan is output through the global dispatching center, and an optimized task allocation table is output through local obstacle avoidance optimization in combination with multi-source sensing data; and calculating task priorities, allocating resources according to the priorities, processing path conflicts, and outputting a final task scheduling instruction. According to the method, the task priority is calculated through the multi-dimensional parameters, and the AGV load is balanced in combination with distributed global computing power scheduling, so that the problem of coexistence of AGV task overload and idle is effectively solved, and operation interruption caused by hardware faults is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automatic sowing of three-dimensional warehouses, and in particular to a multi-layer three-dimensional warehouse automatic sowing path optimization scheduling method and system. BACKGROUND

[0002] With the sustained growth of 15%-20% of the annual business volume of the logistics industry, multi-layer three-dimensional warehouses have become the mainstream warehouse form due to their high-density storage and fast-paced flow characteristics. The efficiency of automatic sowing operations (i.e., the process of accurately transferring materials from multi-layer storage positions to target sowing positions) directly determines the overall operation level of the warehouse. The current existing technology has formed a basic framework of path planning-task allocation-hardware execution, but the path planning mostly uses traditional , Dijkstra algorithm or its simple improved version, with the shortest physical distance as the core target, without integrating material characteristics and inter-layer transfer motion state costs; task scheduling is only based on order urgency sorting, without considering material sensitive attributes; and there is a delay in the interaction between AGV sensing data and algorithms, and the overall ability to adapt to complex scenarios is insufficient.

[0003] There are three major problems in the existing technology: first, the path planning lacks multi-dimensional constraint integration, and the path parameters are not adjusted according to the material size and fragility level, resulting in scratches between long materials and shelves when driving, and a high breakage rate of fragile materials of up to 5%-8%; second, the dynamic environment response is lagging, and the sensing data is mostly uploaded in batches with a 1-second cycle, making it difficult to avoid dynamic obstacles such as inspection vehicles and scattered materials in time, and easily causing AGV congestion and stagnation; third, the task scheduling does not coordinate device load, and only allocates AGV resources according to order urgency, causing some AGVs to be overloaded and some AGVs to be idle, extending the average transfer time of single materials to 12-15 minutes, and the overall operation efficiency can only reach 60%-70% of the designed capacity, making it difficult to meet the actual warehouse demand.

[0004] For example, the publication number CN114648267B 'Automatic three-dimensional warehouse scheduling path optimization method and system', through order priority analysis, space-time map model, cyclic neural network material deployment and time window algorithm, realizes the path optimization of warehouse delivery scheduling, but its technical scheme still has significant limitations in adapting to multi-layer three-dimensional warehouse automatic seeding scene: first, the path planning is only based on the shortest path fitting based on the basic coordinates, without integrating material characteristics (such as size, fragility level) and multi-layer warehouse cross-layer motion state cost, resulting in problems such as long material scratching and fragile material damage still frequently occurring in multi-layer seeding scene; second, dynamic environment response only relies on time window algorithm to handle static path conflicts, without involving AGV real-time load (remaining power, allocated task time consumption) balancing and dynamic obstacles (such as inspection vehicles, scattered materials) real-time avoidance, which is easy to cause local congestion and stagnation when multiple AGVs operate in multi-layer warehouse; third, task scheduling is only based on the basic order of orders, without associating material sensitive attributes (such as fragile, super-long) and device load status, causing some AGVs to be overloaded and some AGVs to be idle, which is difficult to adapt to the special needs of multi-layer warehouse automatic seeding'multi-material, cross-layer section, high precision'. Therefore, it is necessary to design a multi-layer three-dimensional warehouse automatic seeding path optimization scheduling method and system. SUMMARY

[0005] In view of the technical defects in the background art, the present application proposes a multi-layer three-dimensional warehouse automatic seeding path optimization scheduling method and system, which solves the above technical problems and meets the actual needs. The specific technical scheme is as follows: The multi-layer three-dimensional warehouse automatic seeding path optimization scheduling method comprises the following steps: Obtain seeding associated data and three-dimensional warehouse associated data through the data interaction module, and construct a multi-dimensional mapping table; Based on the multi-dimensional mapping table, call the preset material characteristics and path constraint mapping library, filter the paths that meet the constraints through the improved A algorithm and calculate the path cost, and output the initial path; Based on the initial path, input the AGV real-time load status, output the regional task allocation plan through the global scheduling center, combine the multi-source perception data, optimize the local obstacle avoidance, and output the optimized task allocation table; Based on the task allocation table, calculate the task priority, allocate resources according to the priority and handle path conflicts, configure standby AGVs, and output the final task scheduling instruction.

[0006] Further, after outputting the final task scheduling instruction, the following steps are included: Based on the final task scheduling instruction, the AGV extracts the material characteristics and switches the end execution head, and after triple verification, completes the material grabbing, transfer and delivery; In the AGV operation process, when an abnormal material is detected, an abnormal handling process is triggered, and a new final task scheduling instruction is regenerated; After the AGV operation is completed, the seeding accuracy is determined, the warehouse data is updated synchronously, the full-process data is recorded to the algorithm iteration database, and the parameters of the A algorithm and the local obstacle avoidance optimization are optimized and improved based on the algorithm iteration database.

[0007] Further, the specific process of constructing the multi-dimensional mapping table is as follows: Through the data interaction module, the warehouse management system and the production execution system are connected to obtain the seeding task order and the material list, and the order urgency, the target seeding position three-dimensional coordinates, the material size, the material weight and the material fragility level are extracted to generate the seeding associated data; The basic coordinates of the material storage position are read through the RFID tag, and are corrected in combination with the positioning module. The physical existence is assisted to be confirmed through the image of the storage position captured by the industrial camera, and the physical storage position three-dimensional coordinates are generated; Through the environmental monitoring sensor, the temperature, dust concentration and ground state in the three-dimensional warehouse are collected at a set frequency to generate the three-dimensional warehouse associated data; Through the associated unit of the data interaction module, the seeding associated data, the physical storage position three-dimensional coordinates and the three-dimensional warehouse associated data are associated and bound according to the set structure to generate the multi-dimensional mapping table.

[0008] Further, the construction process of the material characteristic and path constraint mapping library is as follows: Through the connection of the enterprise ERP system, the warehouse management system and the material warehousing detection process, the core characteristic parameters of all kinds of materials are collected to form a standardized data set; Based on the physical layout of the three-dimensional warehouse and the operation safety specification, the inherent constraints and dynamic constraint parameters of all paths are extracted to form a quantifiable constraint rule library; Through the multi-dimensional matching algorithm, the standardized data set and the constraint rule library establish a unique mapping relationship to form a structured mapping item, and a material characteristic and path constraint mapping library with material ID as the hash index is established; Based on the material characteristic and path constraint mapping library, simulation verification is carried out through the simulation platform, and the material characteristic and path constraint mapping library is updated based on the simulation verification result.

[0009] Further, the specific steps of outputting the initial path through the improved A algorithm are as follows: Based on the multi-dimensional mapping table, the corresponding constraint parameters are called from the material characteristic and path constraint mapping library, and the candidate paths are selected; Based on the multi-dimensional mapping table, the real-time state of the interlayer transfer unit is extracted, and the cross-layer transfer time is calculated; Based on the improved A algorithm, the estimated total cost of the candidate path is calculated through a path cost calculation formula combined with the cross-layer transport time, and the candidate path with the minimum estimated total cost is selected as the initial path. The path cost calculation formula is as follows: ; Wherein, represents the estimated total cost from the starting node to the target node through the current node n, represents the actual distance cost from the starting node to the current node n, represents the actual transport cost from the starting node to the current node n, represents the actual congestion cost from the starting node to the current node n, represents the heuristic distance cost from the current node n to the target node, represents the heuristic transport cost from the current node n to the target node, represents the heuristic congestion cost from the current node n to the target node, is the distance cost weight coefficient, is the transport cost weight coefficient, is the congestion cost weight coefficient.

[0010] Further, the specific process of the output area task allocation plan is as follows: Through a plurality of distributed regional scheduling nodes, the load state of each stereoscopic warehouse floor AGV is collected in real time, including the remaining power and the total time consumption of the allocated tasks of the AGV, and the CPU usage is monitored, and the load data is fed back to the global scheduling center in real time; Based on the load data and the initial path, when the CPU usage of a certain distributed regional scheduling node is greater than a preset value, it is determined that the computing power is overloaded, the task list currently processed by the distributed regional scheduling node is extracted, and non-core tasks are screened out; Through the global scheduling center, the distributed regional scheduling nodes with CPU usage lower than the preset value are screened out among all the distributed regional scheduling nodes, and the data packets of the screened non-core tasks are migrated to the distributed regional scheduling nodes with CPU usage lower than the preset value through Ethernet; After the task migration is completed, the global scheduling center integrates the task allocation situation of all the distributed regional scheduling nodes, and generates a regional task allocation plan containing the number of AGV task allocations, task types and expected start time of each region.

[0011] Further, the specific process of the local obstacle avoidance optimization is as follows: Obstacle distance data, ground image data and moving target data are collected to generate multi-source perception data, and a dynamic environment map is formed through a local path planning unit; Based on the regional task allocation plan and the dynamic environment map, superimposed analysis is performed through a collision detection module. If a collision risk is detected, the bypass distance is calculated, and if the bypass distance is less than a preset value, the dynamic window method is combined with reinforcement learning to adjust the AGV parameters, and the optimized AGV parameters are output. If the bypass distance is greater than the preset value, the position and movement parameters of the obstacle are fed back to the global scheduling center to trigger incremental re-planning, and an optimized AGV path list is formed. The local path planning unit integrates the optimized AGV parameters and the AGV path list to generate a task allocation table containing the optimized paths of each AGV and the estimated arrival time.

[0012] Further, the task priority calculation process is as follows: Based on the task allocation table, the order urgency, material characteristics and task remaining time of each task are extracted and substituted into the task priority calculation formula to calculate the task priority, and the formula is as follows: ; Wherein, the task priority value, the greater, the higher the task priority, the order urgency coefficient, the material sensitivity coefficient, the task overtime risk coefficient.

[0013] Further, the specific process of outputting the final task scheduling instruction is as follows: Based on the task priority and task allocation table of all tasks, resources with matching path attributes are allocated to each task according to priority from high to low, and a preliminary resource allocation scheme is output. Based on the preliminary resource allocation scheme, the optimized paths of all tasks are compared, and if the AGV path of a low-priority task intersects with the AGV path of a high-priority task and the time overlaps, the low-priority AGV is scheduled to stop at the nearest avoidance area, and then continues to travel after the high-priority task passes, and an updated resource allocation scheme is output. Based on the task priority of all tasks, a standby AGV is bound to a task whose task priority is greater than a preset value, and a redundancy configuration table is output. The preliminary resource allocation scheme, task allocation table and redundancy configuration table are integrated into a structured final task scheduling instruction.

[0014] The multi-layer three-dimensional warehouse automatic seeding path optimization scheduling system comprises: The data interaction module includes a multi-protocol interface, a multi-source data acquisition unit, a data correlation unit and a real-time database, and realizes data transmission through 5G industrial Ethernet to provide basic data support for the whole system. A path planning module, comprising a global path planning unit, a local path planning unit, and a data interaction module that interacts with data in real time, outputs an initial path, a regional task allocation plan, and an optimized path list; A task scheduling module, comprising a priority calculation unit, a resource allocation unit, and a redundancy configuration unit, receives output data from the path planning module, and outputs a final task scheduling instruction to an automatic seeding execution module; An automatic seeding execution module, comprising an intelligent AGV, a modular end execution head, and a visual recognition unit, receives instructions from the task scheduling module, and completes material grabbing, transfer, and delivery; An environment monitoring and fault self-diagnosis module, comprising an environment data monitoring unit, a hardware fault detection unit, a backup resource scheduling unit, and a maintenance work order generation unit, processes exceptions and faults.

[0015] Compared with the prior art, the multi-layer stereoscopic warehouse automatic seeding path optimization scheduling method and system provided by the present application has the following beneficial effects: The present application constructs a multi-dimensional mapping table to associate material characteristics, storage environment and path parameters, calls a material characteristic-path constraint mapping library and combines an improved A algorithm to filter candidate paths, so that the path planning is no longer only targeted at physical distance, but fully adapts to the size, fragility level and other properties of the material, and at the same time, real-time processing of dynamic obstacles is realized through local obstacle avoidance optimization, avoiding the situation of scratching of long materials and shelves, damage of fragile materials and AGV collision congestion, significantly improving the adaptability of path planning and the safety of the operation process.

[0016] The present application aims at the defects of the existing task scheduling that is not coordinated with the equipment load and is low in efficiency, calculates the task priority through multi-dimensional parameters, balances the AGV load in combination with distributed global computing power scheduling, configures a backup AGV for a key task and establishes an exception handling process, effectively solves the problem of coexistence of AGV task overload and idling, avoids the interruption of operation caused by hardware failure, continuously improves the adaptability of the system to complex warehouse scenarios through whole-process data recording and algorithm iterative optimization, overall shortens the material transfer cycle, improves the space utilization rate and operation efficiency of the multi-layer stereoscopic warehouse, reduces the labor and operation cost, and has strong practicality and expandability. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The flowchart of the multi-layer stereoscopic warehouse automatic seeding path optimization scheduling method in the present application.

[0018] Figure 2 The module schematic diagram of the multi-layer stereoscopic warehouse automatic seeding path optimization scheduling system in the present application. DETAILED DESCRIPTION

[0019] In the description of the present application, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "middle", "inner" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" and the like are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined with "first", "second" and the like can explicitly or implicitly include one or more of the features. In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood in a broad sense, for example, it can be a fixed connection, or a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication between the two elements inside. For those skilled in the art, the specific meaning of the above terms in the present application can be understood through specific circumstances.

[0020] The embodiments of the present application will be described below in conjunction with the drawings and related embodiments. The embodiments of the present application are not limited to the following examples, and the present application relates to the necessary components in the related art, which should be regarded as known technology in the art, and is known and mastered by those skilled in the art.

[0021] Referring to Figure 1 The multi-layer three-dimensional warehouse automatic seeding path optimization scheduling method comprises the following steps: Step S100, acquiring seeding associated data and three-dimensional warehouse associated data through a data interaction module, and constructing a multi-dimensional mapping table; The data interaction module interfaces with the warehouse management system, the production execution system and various sensing devices in the three-dimensional warehouse to collect and associate two types of key data, and finally generates a multi-dimensional mapping table. The seeding associated data is obtained from the warehouse management system and the production execution system, including the seeding task order and the material list; the three-dimensional warehouse associated data includes the three-dimensional coordinates of the material storage position and the environmental data in the three-dimensional warehouse; the multi-dimensional mapping table is a structured data table generated by the association unit of the data interaction module, and all key data is bound in a set format.

[0022] Step S200, based on the multi-dimensional mapping table, calling a preset material property and path constraint mapping library, filtering the paths that meet the constraints and calculating the path cost through the improved A algorithm, and outputting the initial path; According to the material data in the multi-dimensional mapping table, a preset material characteristic and path constraint mapping library is called, the path constraint rule corresponding to the current material is matched from the library, and it is ensured that the path is adapted to the material characteristics from the source. According to the constraint rule of the mapping library, the paths that do not meet the conditions are eliminated from all available paths of the stereoscopic warehouse, and the physically feasible, safe and compliant paths are reserved as candidate paths. The estimated total cost of each candidate path is quantitatively calculated by improving the A algorithm, and finally the candidate path with the minimum estimated total cost is selected as the initial path. The improved A algorithm is a path search method for introducing a multi-objective cost function on the basis of the traditional A* algorithm, which is used to find the optimal or approximate optimal path under the condition of meeting the constraint condition. In the application, the improved A algorithm evaluates the search node by extending the heuristic function and fusing multiple weight factors such as distance, time and risk.

[0023] Step S300, based on the initial path, inputting the AGV real-time load state, outputting the regional task allocation plan through the global scheduling center, combining the multi-source perception data, outputting the optimized task allocation table through local obstacle avoidance optimization; The initial path and the AGV real-time load state are input into the global scheduling center, the non-core tasks of the overloaded node are migrated to the low-load node through Ethernet, the task allocation of all nodes is integrated, the regional task allocation plan is output, the task quantity, task type and expected start time of each regional AGV are determined, the calculation power is balanced to avoid equipment overload or idling. The multi-source perception data is a multi-type perception data for capturing the dynamic environment of the stereoscopic warehouse, the local obstacle avoidance optimization is a real-time path adjustment mechanism for dynamic obstacles, the task allocation table is the final path allocation scheme output after local obstacle avoidance optimization, and contains the optimized path and expected arrival time of each AGV. Provide accurate basis for subsequent task priority calculation and resource allocation.

[0024] Step S400, based on the task allocation table, calculate the task priority, allocate resources according to the priority and handle path conflicts, configure backup AGV, and output the final task scheduling instruction.

[0025] The task priority is a comprehensive score for measuring the urgency and importance of task execution, which is used to determine the scheduling order and resource allocation tilt direction. The final task scheduling instruction is an executable instruction output by the task scheduling module, which integrates task priority sorting, AGV allocation result, path parameter and redundant configuration information. The backup AGV refers to the redundant AGV configured for the key task, which is selected from the idle AGV pool according to the nearest principle or performance matching mode.

[0026] In one specific embodiment, the system receives a large number of emergency orders, including fragile household appliances and super-long building materials. The data interaction module obtains the attributes of these materials and their target seeding site information in real time, and constructs a multi-dimensional mapping table combined with the occupancy of each layer channel in the three-dimensional warehouse. The system calls the fragile product speed reduction rule and the super-long piece width limit path strategy stored in the mapping library, uses the improved A algorithm to avoid narrow turning sections, and generates a safe initial path. The global scheduling center allocates tasks reasonably according to the current AGV load distribution, and combines the temporary obstacles discovered by the laser radar to optimize and adjust the driving trajectory through local obstacle avoidance. For high-priority orders, the system automatically assigns nearby idle AGVs as backups, which immediately take over the task in case of main vehicle failure. After the final scheduling instruction is issued, the AGV group cooperates to complete the efficient and zero-damage automatic seeding operation.

[0027] The present application realizes the technical effects of shortening the material transfer cycle, improving the space utilization rate and the operation stability by obtaining seeding associated data and three-dimensional warehouse associated data, constructing a multi-dimensional mapping table, calling the material characteristics and path constraint mapping library, and generating an initial path through the improved A algorithm, inputting the real-time load state of the AGV and generating a regional task allocation plan through the global scheduling center, combining multi-source perception data for local obstacle avoidance optimization to output a task allocation table, calculating the task priority, handling path conflicts and configuring a backup AGV based on the task allocation table to output the final scheduling instruction, realizing path constraint response by fusing material characteristics and warehouse environment information, improving path safety by embedding multi-dimensional cost evaluation with the improved A algorithm, reducing collision risk by using multi-source perception data to support dynamic obstacle avoidance, and realizing reasonable allocation of resources by combining task priority and load state, and enhancing system fault tolerance by configuring a backup AGV.

[0028] In one embodiment of the present application, after outputting the final task scheduling instruction, the following steps are included: Step S500, based on the final task scheduling instruction, the AGV extracts the material characteristics and switches the end execution head, and after triple verification, completes the material grabbing, transfer and dropping; After receiving the final task scheduling instruction, the AGV parses the core characteristic parameters of the current material from the instruction, specifically including the material's weight, size, shape, and fragility level. It then calls the preset material characteristic-end effect head mapping rules to automatically switch to the appropriate modular end effect head. Specifically, for ultra-light materials, it switches to a vacuum anti-slip suction cup; for irregularly shaped materials, it switches to a multi-degree-of-freedom mechanical claw; and for extra-long or extra-heavy materials, it switches to a forklift-type end effect head. Once the AGV reaches the material storage location, it initiates a triple verification mechanism to avoid picking up the wrong material or deviating from the path. The triple verification includes visual verification, RFID verification, and positioning verification. Specifically, it scans the material's barcode or QR code with an industrial camera to obtain the material ID and compares it with the material ID in the scheduling instruction; it reads the material tag information using an RFID reader to confirm that the material's weight and fragility level match the instruction; and it uses a UWB positioning module to confirm that the current storage location coordinates match the storage location coordinates in the instruction. After passing the triple verification, the end effector grabs materials according to the preset parameters; the AGV strictly follows the optimized path in the scheduling instructions and receives dynamic environmental data from the local path planning unit in real time during the journey, and fine-tunes the driving speed and direction; after reaching the target seeding position, it completes the placement according to the preset placement accuracy, and then sends a work progress signal back to the task scheduling module.

[0029] Step S600: When an abnormality is detected during AGV operation, the abnormality handling process is triggered, and a new final task scheduling instruction is regenerated. In the triple verification process, if the AGV fails to recognize the barcode, the RFID information is inconsistent with the instructions, or the positioning deviation is greater than ±15mm, it is immediately identified as a material abnormality. The current action is stopped and a local alarm is triggered. The preset emergency path for abnormal materials is invoked to transfer the abnormal materials to the abnormal material processing area. The staff scans the materials using a handheld PDA terminal or manually enters the material ID, weight, size, and other correction information. The PDA terminal uploads the corrected data to the data interaction module in real time. The data interaction module updates the multi-dimensional mapping table and re-triggers the generation of a new final task scheduling instruction, scheduling the AGV to re-execute the operation to ensure that materials do not accumulate.

[0030] When an AGV detects a malfunction, follow these steps to handle it: The AGV battery management system monitors the remaining power in real time (low power fault is determined when SOC < 22V), the motor controller monitors the speed deviation (motor fault is determined when > ±5%), and the fault information is transmitted to the environmental monitoring and fault self-diagnosis module in real time via the vehicle Ethernet. The fault self-diagnosis module generates fault codes and synchronizes them to the redundant configuration unit of the task scheduling module; The redundant configuration unit calls the binding relationship between the task and the standby AGV, activates the standby AGV within 10 seconds, and issues a fault point takeover command to the standby AGV. The fault self-diagnosis module automatically generates a maintenance work order and sends it to the terminal of the operation and maintenance personnel, and after the maintenance is completed, a 100m path test needs to be performed, and after passing, the system can be reconnected.

[0031] Step S700, after the AGV operation is completed, the seeding accuracy is determined, the warehouse data is updated synchronously, the full-process data is recorded to the algorithm iteration database, and the parameters of the improved A algorithm and the local obstacle avoidance optimization are optimized based on the algorithm iteration database.

[0032] After the material is put in, the visual recognition unit shoots an image of the target seeding position, confirms the material quantity, position and scheduling instruction requirement through image comparison algorithm, ensures the seeding accuracy, the data interaction module synchronizes the seeding completion signal to the warehouse management system, updates the material inventory data in the warehouse management system, and synchronizes it to the transportation management system, the transportation management system generates an outbound transportation plan based on the signal, and realizes the full-link data connection from seeding to outbound. The full-process data includes path planning data, obstacle avoidance optimization data, operation execution data and exception handling data, the algorithm iteration module is started regularly, the data in the algorithm iteration database is statistically analyzed, the path cost deviation rate of the improved A algorithm and the obstacle avoidance success rate of the local obstacle avoidance optimization are calculated, if the path cost deviation rate is greater than 10%, the weight coefficient of the improved A algorithm is adjusted, and if the frequency of dynamic obstacles appears is greater than 5 times / hour, the refresh rate of the local obstacle avoidance optimization is increased from 10 times / s to 15 times / s.

[0033] The application extracts the material characteristics and switches the end execution head according to the instruction after outputting the final task scheduling instruction, combines the triple verification mechanism to complete material grabbing, transfer and putting, detects abnormalities or faults in real time during operation and triggers the exception handling process to regenerate the scheduling instruction, determines the seeding accuracy, updates the warehouse data and stores the full-process data into the algorithm iteration database after the operation is completed, and then adjusts the parameters of the improved A algorithm and the local obstacle avoidance optimization based on the database, dynamically adapts the grabbing tool, verifies the operation accuracy, autonomously reconstructs the scheduling task under abnormal conditions, and optimizes the algorithm parameters by using actual operation data, so that the technical effects of improving the running stability, task continuity and system self-evolution ability of the multi-layer three-dimensional warehouse in a complex and high requirement operation environment can be achieved.

[0034] In an embodiment of the application, the specific process of constructing the multi-dimensional mapping table is as follows: Step S101, the data interaction module is connected to the warehouse management system and the production execution system to obtain the seeding task order and the material list, extract the order urgency, the three-dimensional coordinates of the target seeding position, the material size, the material weight and the material fragility level, and generate the seeding associated data; The warehouse management system is an information system responsible for managing warehouse operation processes, inventory status and order execution, and can be used to output seeding task orders and other operation instruction information. The production execution system is an industrial information system used to control and track material flow and operation execution in the manufacturing process, and can be used to provide production-related material lists and task association information. The seeding task order is an operation instruction indicating the transfer of a specific material from a storage location to a designated seeding location, and can be used as the task source for automatic seeding operations. The material list is a data table listing all materials required for a task and their attributes, and can be used to provide key attributes such as material size, weight, and fragility level. The order urgency is a priority indicator reflecting the time limit requirement for task completion, and can be used to affect priority calculation in task scheduling. The target seeding location three-dimensional coordinates are the precise X / Y / Z coordinates of the target seeding location in the three-dimensional warehouse space, and can be used for end point positioning in path planning.

[0035] Step S102, read the basic coordinates of the material storage location through the RFID tag, correct them with the positioning module, take an image of the storage location with an industrial camera to assist in confirming physical existence, and generate the physical storage location three-dimensional coordinates; An RFID tag is pre-installed at each layer of the material storage location. The RFID reader reads the storage location basic coordinates stored in the tag memory through wireless radio frequency technology. These coordinates are the theoretical coordinates pre-set during warehouse construction, with an installation deviation of within ±50 mm. The positioning module is called to correct the basic coordinates read by the RFID in real time: the UWB positioning module communicates with the pre-set positioning base station in the warehouse to calculate the actual three-dimensional coordinates of the storage location, correct the deviation to within ±10 mm, and ensure that the coordinates match the physical location perfectly. An industrial camera is started to take an image of the material storage location, and an image recognition algorithm is used to confirm whether the storage location actually stores materials, avoiding the problem of mis-scheduling empty storage locations. If no materials are detected, the storage location is immediately marked as empty and feedback is provided to the warehouse management system to update the inventory status. The corrected three-dimensional coordinates and physical existence markers are integrated to generate the material storage location three-dimensional coordinates.

[0036] Step S103, collect the temperature, dust concentration and ground state in the three-dimensional warehouse at a set frequency through environmental monitoring sensors to generate three-dimensional warehouse correlation data; The temperature in the three-dimensional warehouse is measured by a thermocouple sensor, the dust concentration is measured by a laser dust sensor, and the ground state is measured by a combination of ultrasonic and infrared sensors to detect ground flatness.

[0037] Step S104, associate and bind the seeding association data, physical storage location three-dimensional coordinates and three-dimensional warehouse correlation data in a set structure through the association unit of the data interaction module to generate a multi-dimensional mapping table.

[0038] The association unit of the data interaction module uniquely binds the above three types of data in a preset structured format, generates a multi-dimensional mapping table, and the association unit performs integrity checking on the bound data; after the checking passes, the multi-dimensional mapping table is stored in the real-time database and is synchronized to the path planning module as the unique data input source for initial path planning.

[0039] The application obtains the seeding task order and the material list by docking the warehouse management system and the production execution system, extracts the order urgency, the three-dimensional coordinates of the target seeding site, the material size, the material weight and the material fragility level from the order to generate seeding associated data, reads the basic coordinates of the material storage site through the RFID tag and corrects them in combination with the positioning module, simultaneously confirms the physical existence through the image of the storage site taken by the industrial camera to generate the three-dimensional coordinates of the physical storage site, collects the temperature, dust concentration and ground state in the three-dimensional warehouse at a set frequency through the environmental monitoring sensor to generate three-dimensional warehouse associated data, and then associates and binds the seeding associated data, the three-dimensional coordinates of the physical storage site and the three-dimensional warehouse associated data through the association unit of the data interaction module to generate a multi-dimensional mapping table, so that the system can comprehensively model the automatic seeding task based on multi-dimensional information such as task attributes, material characteristics, actual positions and environmental states, and can improve the data integrity, accuracy and environmental adaptability of path planning and scheduling decision-making.

[0040] In an embodiment of the application, the construction process of the material characteristics and path constraint mapping library is as follows: Step S201, collect the core characteristic parameters of all categories of materials through the docking of enterprise ERP systems, warehouse management systems and material warehousing detection processes, and form a standardized data set; Docking the enterprise ERP system extracts the material basic information, ensures the traceability of the material identity, and docking the warehouse management system supplements the material storage history data to assist subsequent constraint rule adaptation, and the material warehousing detection process is a physical property detection and quality checking procedure performed when the material enters the warehouse, which can be used to generate accurate initial material characteristic data. In the material warehousing detection process, the length, width and height of the material are collected by a laser size measuring instrument, the weight m of the material is collected by an electronic scale, the material fragility level is determined by a material quality detection report, the material form is determined by a visual detection device, and special attributes are input through a material specification book.

[0041] Step S202, based on the physical layout of the three-dimensional warehouse and the operation safety specification, extract all inherent and dynamic constraint parameters of the path to form a quantifiable constraint rule library; The physical parameters of all paths and transfer units are obtained through three-dimensional laser scanning and warehouse layout paper analysis, including channel attributes and transfer unit attributes, the path attributes are converted into rigid constraints corresponding to the material characteristics, a constraint rule library is formed, including size constraints, load-bearing constraints, speed constraints and special constraints. Inherent constraints are path limitation conditions determined by the physical structure of the three-dimensional warehouse and cannot be changed, which can be used to define the basic traffic capacity of the path, such as height limit and turning radius. Dynamic constraints are path usage limitation conditions that change with the operation process, which can be used to reflect the availability and risk level of the path in a specific period.

[0042] Step S203, by a multi-dimensional matching algorithm, a unique mapping relationship between the standardized data set and the constraint rule library is established, a structured mapping entry is formed, and a material characteristic and path constraint mapping library with material ID as a hash index is established; The multi-dimensional matching algorithm is a calculation method for semantic alignment and logical association of material characteristics and path constraints, which can be used to automatically generate unique mapping entries of material ID to path constraints. The structured mapping entry is a data unit representing the relationship between a material and its corresponding path constraint, which can be used as the basic storage unit of the material characteristic and path constraint mapping library.

[0043] Step S204, based on the material characteristic and path constraint mapping library, simulation verification is performed through a simulation platform, and the material characteristic and path constraint mapping library is updated based on the simulation verification result.

[0044] The simulation platform is a digital twin environment for simulating the whole process of warehouse operation, which can be used to verify the effectiveness and safety of the mapping library in complex scenarios. Taking the path adaptation of a new large-size refrigerator in the initial stage of warehouse entry as an example, the system obtains the basic information of the material through ERP, and forms a standardized entry combined with the actual measurement data of the warehouse entry detection station. The inherent constraint of "prohibiting long materials from passing through narrow curves" has been defined in the constraint rule library. The multi-dimensional matching algorithm identifies that the refrigerator belongs to the "long" category and automatically maps it to the wide channel dedicated path set. The simulation platform simulates the transfer process of the material during the peak period and finds that the original path conflicts with the inspection vehicle dynamic line, triggering dynamic constraint upgrade. The system updates the mapping entry accordingly, adds the avoidance time limit, and synchronizes the optimized rules to the production environment to ensure the safe completion of the first single operation.

[0045] It should be noted that the specific steps of outputting the initial path by improving the A algorithm are as follows: Step S205, based on the multi-dimensional mapping table, the corresponding constraint parameters are called from the material characteristic and path constraint mapping library, and the candidate paths are screened out; Extract the core characteristic parameters of the current material from the multidimensional mapping table, match the unique corresponding constraint parameters in the mapping library according to the material core characteristic parameters, eliminate the paths that do not meet the above constraint parameters from all available paths of the stereoscopic warehouse, and only keep the physically feasible, safe and compliant paths to form a candidate path set.

[0046] Step S206, based on the multidimensional mapping table, the real-time state of the interlayer transfer unit is extracted, and the cross-layer transfer time is calculated; When the transfer unit has no task at present, the basic transfer time consumption is a fixed value, the transfer waiting time consumption is 0, the transfer unit has a task queue at present, the estimated completion time of the previous task in the queue is extracted from the multidimensional mapping table, and the transfer waiting time is obtained, .

[0047] Step S207, based on the improved A algorithm, the estimated total cost of the candidate path is calculated through the path cost calculation formula combined with the cross-layer transfer time, and the candidate path with the minimum estimated total cost is selected as the initial path; The path cost calculation formula is as follows: ; Among them, represents the estimated total cost from the starting node to the target node through the current node n, represents the actual distance cost from the starting node to the current node n, Manhattan distance is used for the same layer path, and the vertical distance cost between layers is added for the cross-layer path; represents the actual transfer cost from the starting node to the current node n, that is, the cross-layer transfer time calculated in step S206; represents the actual congestion cost from the starting node to the current node n, the real-time AGV density of the road section where the current node n is located is extracted from the multidimensional mapping table, and then , = the number of AGVs on the current road section / the maximum number of AGVs that the road section can accommodate, the maximum number of AGVs that the road section can accommodate = the length of the road section / 1.5m, and 1.5m is the width of the channel occupied by a single AGV; represents the heuristic distance cost from the current node n to the target node, and the calculation logic is consistent with , only the starting node coordinates are replaced by the target seeding position coordinates; represents the heuristic transfer cost from the current node n to the target node, and the calculation logic is consistent with , the transfer waiting time is replaced by the average waiting time in the past 1 hour; represents the heuristic congestion cost from the current node n to the target node, and the calculation logic is consistent with Consistent Replace with the 1-hour historical average AGV density; This is the distance cost weighting coefficient. This is the weighting coefficient for transportation costs. This is the congestion cost weighting coefficient.

[0048] This invention uses a multi-dimensional mapping table to retrieve constraint parameters to filter candidate paths, extracts the real-time status of inter-level transfer units to calculate cross-level transfer time, combines this time with the path cost calculation formula to evaluate the estimated total cost of each candidate path, and selects the path with the minimum cost as the initial path. By integrating material characteristics, real-time equipment status, and a multi-dimensional weighted cost model, it achieves refined modeling of path decision-making. Through dynamic weight configuration and collaborative calculation of actual / heuristic costs, it improves the algorithm's adaptability to the three-dimensional warehousing environment, achieving the technical effect of generating high-quality initial paths that balance transportation safety, vertical movement efficiency, and traffic load balance under high-density operation conditions.

[0049] In one embodiment of the present invention, the specific process of the output area task allocation plan is as follows: Step S301: Collect the load status of AGVs on each floor of the automated warehouse in real time through several distributed regional scheduling nodes, including the remaining power of the AGVs and the total time of the assigned tasks, and monitor their own CPU utilization, and generate load data to be fed back to the global scheduling center in real time. By using distributed regional scheduling nodes divided according to the floors of the automated warehouse, the load status of AGVs and their own computing power data within the jurisdiction are collected in real time, forming a load data pool for the entire system. Each regional node feeds back the collected AGV load status and its own CPU utilization rate to the global scheduling center in real time via 5G industrial Ethernet. The global scheduling center stores the data in a real-time database according to the structured format of "region number-timestamp-AGVID-load parameter-computing power parameter", forming a dynamically updatable "global load view" to provide data support for subsequent computing power judgment and task allocation.

[0050] Step S302: Based on load data and initial path, when the CPU utilization of a certain distributed area scheduling node is greater than the preset value, it is determined to be computing power overload. Extract the list of tasks currently being processed by the distributed area scheduling node and filter out non-core tasks. The global scheduling center sets a CPU usage rate threshold, checks each regional node one by one, if the CPU usage rate of the node is greater than or equal to 80%, it is determined that the computing power is overloaded, at this time, the node has appeared delay in processing the current task, and needs to relieve the pressure through task migration; if the CPU usage rate of the node is 20% to less than 80%, it is determined that it is "normal load", and the current task processing state is maintained without migration; if the CPU usage rate of the node is less than 20%, it is determined that it is "low load", and is marked as a potential migration target for subsequent non-core tasks. For the computing power overloaded node, the global scheduling center extracts a list of all tasks currently processed by the node, and screens out non-core tasks in combination with the material characteristics and order urgency in the multi-dimensional mapping table.

[0051] In step S303, the global scheduling center screens out a distributed regional scheduling node with a CPU usage rate lower than a preset value from all distributed regional scheduling nodes, and migrates a data packet of the screened non-core task to the distributed regional scheduling node with the CPU usage rate lower than the preset value through Ethernet. The global scheduling center matches a low-load regional node or a cloud computing power node in the whole system, completes the migration of the non-core task through a reliable data transmission mechanism, ensures the task breakpoint continuation and data consistency, and selects a migration target according to the "regional proximity principle and computing power redundancy principle", preferentially matches a low-load regional node on the same floor or adjacent floor as the overloaded node, and reduces the cross-regional data transmission delay; if the number of low-load regional nodes is insufficient, the cloud computing power node is called, and is connected to the global scheduling center through a public network special line, to ensure the flexibility of computing power supplement.

[0052] In step S304, when the task migration is completed, the global scheduling center integrates the task allocation situation of all distributed regional scheduling nodes, and generates a regional task allocation plan containing the number of AGV task allocations, the task type and the predicted start time of each region.

[0053] The global scheduling center integrates the task processing states of all regional nodes, generates a structured regional task allocation plan, and provides a task allocation benchmark for subsequent local obstacle avoidance optimization. For example, during the lunch peak period, the CPU usage rate of the third layer distributed scheduling node rises to 90% due to processing a large number of seeding tasks, triggering the computing power overload determination. The node immediately reports the state, the global scheduling center identifies and migrates 12 non-core replenishment tasks being processed by the node to the idle scheduling nodes with CPU usage rates lower than 20% on the first and fifth layers after receiving the feedback. After the migration is completed, each region synchronizes the task list, and the global scheduling center generates a new regional task allocation plan according to this, containing the number of tasks, the type and the predicted start time of each layer of AGV, avoiding the overall efficiency decline caused by local congestion.

[0054] The application collects the load state of each stereoscopic warehouse floor AGV in real time through several distributed regional scheduling nodes and monitors the CPU usage rate of itself, generates load data feedback to the global scheduling center; based on the load data and the initial path, when the CPU usage rate exceeds the preset value, it is determined that the computing power is overloaded, and non-core tasks are screened out; through the global scheduling center, the idle scheduling node with lower CPU usage rate is selected, and the non-core tasks are migrated to the target node by using Ethernet; after the migration is completed, the global scheduling center integrates the task allocation of each node to generate a regional task allocation plan, identifies local computing power bottlenecks through double index cooperative monitoring, automatically strips non-critical tasks and realizes cross-node computing power rebalancing, and finally forms a unified and coordinated task allocation scheme, which can improve the stability and response capability of the system in a high-concurrency scenario, and enhance the technical effects of scheduling architecture flexibility and fault tolerance.

[0055] It should be noted that the specific process of the local obstacle avoidance optimization is as follows: Step 305, collect obstacle distance data, ground image data and moving target data, generate multi-source perception data, and form a dynamic environment map through a local path planning unit; The laser radar on the AGV collects the distance and contour data of the obstacles around the AGV to generate obstacle distance data, the panoramic camera collects ground image data, and the fixed perception device in the warehouse collects moving target data, and the local path planning unit unifies the formats of the above data to generate multi-source perception data, and generates a high-frequency updated dynamic environment map after fusion processing, which realizes accurate description of the state of obstacles.

[0056] Step 306, based on the regional task allocation plan and the dynamic environment map, superimposed analysis is performed through a collision detection module; The collision detection module is an analysis component for evaluating whether the AGV scheduled path and the obstacles in the dynamic environment map exist spatial overlap, which can be used to identify potential conflict points in advance and trigger corresponding obstacle avoidance strategies.

[0057] Step 307, if a collision risk is detected, calculate the detour distance, if the detour distance is less than a preset value, use the dynamic window method combined with reinforcement learning to adjust the AGV parameters, and output the optimized AGV parameters; The detour distance is the additional distance required to bypass the obstacle from the current position when the main path is blocked, which can be used as a basis for decision-making to select local adjustment or global re-planning. The dynamic window method combined with reinforcement learning is a speed and space constraint obstacle avoidance technology that combines classical control methods and intelligent learning mechanisms, which can be used to achieve smooth obstacle avoidance under real-time parameter adjustment and adapt to complex local scenarios. The collision detection method is as follows: the planning path node sequence of a single AGV and the expected arrival time of each node are extracted from the regional task allocation plan, and the obstacle information within a 5m range around the AGV planning path at the current timestamp is extracted from the dynamic environment map. If the AGV travels according to the planning path, the distance between the AGV and the obstacle at a certain time is <0.5m, and the time difference is ≤2s, it is determined that there is a collision risk. The specific method of adjusting the AGV parameters is as follows: the ground state is extracted from the dynamic environment map. If it is an "oil stain area", the AGV travel speed is reduced to 0.7 times the original speed, and the brake distance is extended to 1.5 times the original brake distance. If it is a "joint raised area", the AGV travel speed is reduced to 0.5 times the original speed, a reward function is introduced, the parameter combination with the smallest detour distance and the smallest speed loss is selected, and the optimized AGV travel speed, direction, and brake distance parameters are output and sent to the AGV controller for execution.

[0058] Step 308, if the detour distance is greater than the preset value, the position and movement parameters of the obstacle are fed back to the global scheduling center, triggering incremental re-planning, and forming an optimized AGV path list; Incremental re-planning only re-plans the "obstacle affected section" rather than the entire path, reducing the consumption of computing power. The improved A algorithm is used in combination with the obstacle constraints in the dynamic environment map to recalculate the node sequence of the path section, generate a new section, splice the new section with the "non-affected section" of the original planning path, and form a complete optimized AGV path list, avoiding the time waste caused by full path re-planning.

[0059] Step 309, integrate the optimized AGV parameters and AGV path list through the local path planning unit to generate a task allocation table containing the optimized AGV path and the expected arrival time of each AGV.

[0060] In one specific embodiment, during the concentrated outbound phase at night, a toolbox temporarily stored in a certain aisle for equipment maintenance was identified as a static obstacle by the LiDAR. The local path planning unit integrates this distance data with images of water accumulation on the ground captured by a camera to construct a dynamic environment map. The collision detection module detects that the original path will pass through this area and calculates the detour distance. Since the detour requires crossing three layers of shelving aisles, the distance exceeds a preset threshold. The system feeds back the obstacle coordinates and the blocked area to the global scheduling center, triggering incremental replanning. The scheduling center reallocates adjacent available paths and issues a new path list. Simultaneously, the affected AGVs smoothly dock and await instruction updates based on deceleration and fine-tuning steering parameters output by the dynamic window method combined with reinforcement learning. After the new path is issued, the local unit integrates the parameters and path to generate a task allocation table containing the estimated arrival time, ensuring that the overall operation rhythm is not significantly affected.

[0061] It should be noted that the calculation process for the task priority is as follows: Based on the task allocation table, extract the order urgency, material characteristics, and remaining time for each task, and substitute them into the task priority calculation formula to calculate the task priority, as follows: ; in, This represents the task priority value. The larger the value, the higher the task priority. This is the order urgency coefficient, which corresponds one-to-one with the order urgency level. Urgency level 5 → α=5.0, level 4 → α=4.0, level 3 → α=3.0, level 2 → α=2.0, level 1 → α=1.0. The assignment logic is that "for every increase of urgency level, the coefficient increases by 1.0", ensuring that the priority weight of urgent orders is significantly higher than that of ordinary orders. The material sensitivity coefficient distinguishes between sensitive materials and ordinary materials. Materials with "fragile grade ≥ 4" or "extra-long materials" → β = 1.5, and other ordinary materials → β = 1.0. The assignment logic is that "sensitive materials need to have an additional priority weight" to avoid damage or delays in transportation of sensitive materials due to resource contention. The task timeout risk coefficient is linked to the remaining time of the task. If the remaining time is ≤1 hour, γ=2.0; if 1 hour < remaining time ≤2 hours, γ=1.0; if the remaining time is >2 hours, γ=0.5. The assignment logic is that "the shorter the remaining time, the higher the timeout risk and the larger the coefficient", which forces the system to prioritize tasks that are about to expire, thereby reducing the risk of order default.

[0062] 9. The multi-layer automated seeding path optimization and scheduling method for warehouses according to claim 8, characterized in that the specific process of outputting the final task scheduling instruction is as follows: Step S401: Based on the task priority and task allocation table of all tasks, allocate resources matching path attributes to each task in descending order of priority, and output a preliminary resource allocation scheme. Resources are allocated in descending order of task priority. High-priority tasks are given priority to use "premium resources", including wide channels, idle inter-layer transfer units, and AGVs with ≥80% remaining power. Low-priority tasks are allocated "regular resources", including standard channels, idle transfer units, and AGVs with ≥50% remaining power.

[0063] Step S402: Based on the preliminary resource allocation scheme, compare the optimized paths of all tasks. If the AGV path of the low-priority task intersects with the AGV path of the high-priority task and the time overlaps, schedule the low-priority AGV to stop at the nearest avoidance zone, and continue driving after the high-priority task passes. Output the updated resource allocation scheme. The system retrieves the AGV path node coordinates and estimated arrival times from the task allocation table and compares the paths of different AGVs. If the AGV paths of low-priority tasks and high-priority tasks intersect at a node, and the time difference between their arrival times at that node is ≤2, a path conflict is identified. Following the principle of lower-priority tasks yielding to higher-priority tasks, if both conflicting tasks are high-priority, the task with the shorter remaining time takes priority; if both are low-priority, the task allocated resources first takes priority. For low-priority AGVs that need to yield, the system automatically matches the nearest yield zone from the warehouse layout database and generates a docking yield zone instruction.

[0064] Step S403: Based on the task priority of all tasks, bind a spare AGV to the task whose task priority is greater than the preset value, and output the redundancy configuration table; Configure 10% of the total number of AGVs in the automated warehouse as standby AGVs. The standby AGVs must be in standby mode. Bind one standby AGV to each critical task to form an association table of "task ID-primary AGVID-standby AGVID" and store it in the redundancy configuration unit of the task scheduling module to ensure that the standby resources can be quickly located in case of failure.

[0065] Step S404: Integrate the preliminary resource allocation scheme, task allocation table, and redundancy configuration table into a structured final task scheduling instruction.

[0066] See Figure 2 The present invention also provides an automatic seeding path optimization and scheduling system for multi-layer automated warehouses, comprising: The data interaction module includes multi-protocol interfaces, multi-source data acquisition units, data association units, and a real-time database. It achieves data transmission through 5G industrial Ethernet, providing basic data support for the entire system. The multi-protocol interface supports two commonly used industrial protocols, HTTP and MQTT, which respectively connect to the warehouse management system, production execution system, and transportation management system. It extracts sowing task orders and bill of materials data from the warehouse management system and production execution system via HTTP, and synchronizes sowing completion information to the transportation management system via MQTT, thus resolving protocol barriers for data interaction between different systems. The multi-source data acquisition unit includes multi-source unit cells and environmental monitoring sensors, collecting material storage location coordinates and warehouse environmental data at a set frequency to ensure data real-time performance. The data association unit generates a multi-dimensional mapping table, binding data according to the structure of "material ID - sowing association data - storage location coordinates - environmental data," and automatically verifies data integrity.

[0067] The path planning module includes a global path planning unit and a local path planning unit. It interacts with the data interaction module in real time and outputs the initial path, regional task allocation plan, and optimized path list. The global path planning unit is responsible for initial path planning and global computing power scheduling, while the local path planning unit deploys dynamic windowing and reinforcement learning algorithms and is responsible for local obstacle avoidance optimization.

[0068] The task scheduling module includes a priority calculation unit, a resource allocation unit, and a redundancy configuration unit. It receives the output data from the path planning module and outputs the final task scheduling instruction to the automatic seeding execution module. The priority calculation unit is used to calculate the priority of each task, the resource allocation unit is used to allocate resources according to the priority, and the redundancy configuration unit is used to configure backup AGVs for high-priority tasks.

[0069] The automatic seeding execution module includes an intelligent AGV, a modular end effector, and a vision recognition unit. It receives instructions from the task scheduling module and completes material grabbing, transfer, and delivery. The automated seeding execution module serves as an operational terminal for the physical handling and placement of materials, translating scheduling instructions into actual operational actions. Equipped with a modular end effector on an intelligent AGV, the module precisely grasps and places materials under the guidance of a vision recognition unit.

[0070] The environmental monitoring and fault self-diagnosis module includes an environmental data monitoring unit, a hardware fault detection unit, a backup resource scheduling unit, and a maintenance work order generation unit, which handles anomalies and faults.

[0071] The environmental data monitoring unit collects real-time data on warehouse temperature, dust concentration, and ground conditions, synchronizing the data to the data interaction module to provide environmental constraints for path planning. The hardware fault detection unit monitors the status of intelligent AGVs and inter-level transfer units in real time, generating fault codes containing "faulty equipment ID, type, and location." Upon receiving the fault code, the backup resource scheduling unit calls upon the backup AGV or backup transfer unit from the task scheduling module within 10 seconds to take over the tasks of the faulty equipment, ensuring uninterrupted operation. The maintenance work order generation unit automatically generates maintenance work orders based on the fault codes, including fault cause analysis, required repair parts models, and repair steps, and sends them to the maintenance personnel's terminal.

[0072] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing and scheduling the automatic seeding path in a multi-layered automated warehouse, characterized in that: Includes the following steps: The data interaction module is used to obtain seeding-related data and three-dimensional warehouse-related data, and a multi-dimensional mapping table is constructed. Based on a multi-dimensional mapping table, a pre-defined material property and path constraint mapping library is called. An improved A algorithm is used to filter paths that meet the constraints and calculate path costs, and output the initial path. Based on the initial path, the real-time load status of the AGV is input, and the regional task allocation plan is output through the global scheduling center. Combined with multi-source perception data, the optimized task allocation table is output through local obstacle avoidance optimization. Based on the task allocation table, calculate task priorities, allocate resources according to priorities and handle path conflicts, configure backup AGVs, and output the final task scheduling instructions.

2. The method for optimizing and scheduling the automatic seeding path of a multi-layered automated warehouse according to claim 1, characterized in that, After outputting the final task scheduling instruction, the following steps are also included: Based on the final task scheduling instruction, the AGV extracts material characteristics and switches the end execution head, and completes material grabbing, transfer and delivery after triple verification; When an abnormality is detected during AGV operation, the abnormality handling process is triggered, and a new final task scheduling instruction is regenerated. After the AGV operation is completed, the seeding accuracy is determined, the warehouse data is updated synchronously, and the entire process data is recorded in the algorithm iteration database. Based on the algorithm iteration database, the parameters of the A algorithm and the local obstacle avoidance optimization are optimized and improved.

3. The method for optimizing and scheduling the automatic seeding path of a multi-layered automated warehouse according to claim 1, characterized in that, The specific process for constructing the multi-dimensional mapping table is as follows: By connecting the data interaction module with the warehouse management system and the production execution system, the system obtains the sowing task orders and material lists, extracts the order urgency, the three-dimensional coordinates of the target sowing position, the material size, the material weight, and the material fragility level, and generates sowing-related data. The basic coordinates of the material storage location are read by RFID tags, corrected by the positioning module, and the physical existence is confirmed by taking images of the storage location by an industrial camera, thus generating the three-dimensional coordinates of the physical storage location. The temperature, dust concentration and ground condition inside the automated storage and retrieval system are collected at a set frequency by environmental monitoring sensors to generate associated data for the automated storage and retrieval system. By using the association unit of the data interaction module according to the set structure, the seeding association data, the three-dimensional coordinates of the physical storage location, and the three-dimensional warehouse association data are associated and bound together to generate a multi-dimensional mapping table.

4. The method for optimizing and scheduling the automatic seeding path of a multi-layered automated warehouse according to claim 1, characterized in that, The construction process of the material property and path constraint mapping library is as follows: By connecting with enterprise ERP systems, warehouse management systems and material receiving and inspection processes, core characteristic parameters of all categories of materials are collected to form a standardized dataset. Based on the physical layout and operational safety specifications of the automated warehouse, the inherent and dynamic constraint parameters of all paths are extracted to form a quantifiable constraint rule base. By using a multi-dimensional matching algorithm, a unique mapping relationship is established between the standardized dataset and the constraint rule base, forming structured mapping entries, and a material characteristic and path constraint mapping base is established with the material ID as the hash index. Based on the material property and path constraint mapping library, simulation verification is performed through a simulation platform, and the material property and path constraint mapping library is updated based on the simulation verification results.

5. The method for optimizing and scheduling the automatic seeding path of a multi-layered automated warehouse according to claim 1, characterized in that, The specific steps for outputting the initial path using the improved A algorithm are as follows: Based on the multi-dimensional mapping table, the corresponding constraint parameters are retrieved from the material properties and path constraint mapping library to filter out candidate paths. Based on a multi-dimensional mapping table, the real-time status of the inter-layer transfer unit is extracted, and the cross-layer transfer time is calculated. Based on the improved A algorithm, combined with cross-layer transfer time, the estimated total cost of candidate paths is calculated using the path cost calculation formula, and the candidate path with the minimum estimated total cost is selected as the initial path. The formula for calculating the path cost is as follows: ； in, This represents the estimated total cost from the starting node through the current node n to the target node. This represents the actual distance cost from the starting node to the current node n. This represents the actual transit cost from the starting node to the current node n. This represents the actual congestion cost from the starting node to the current node n. This represents the heuristic distance cost from the current node n to the target node. This represents the heuristic transfer cost from the current node n to the target node. Let represent the heuristic congestion cost from the current node n to the target node. This is the distance cost weighting coefficient. This is the weighting coefficient for transportation costs. This is the congestion cost weighting coefficient.

6. The method for optimizing and scheduling the automatic seeding path of a multi-layered automated warehouse according to claim 1, characterized in that, The specific process of the output area task allocation plan is as follows: The load status of AGVs on each floor of the automated warehouse is collected in real time through several distributed regional scheduling nodes, including the remaining power of the AGVs and the total time of the assigned tasks, and its own CPU utilization is monitored. The load data is then fed back to the global scheduling center in real time. Based on load data and initial path, when the CPU utilization of a certain distributed area scheduling node exceeds the preset value, it is determined to be computing power overload. The list of tasks currently being processed by the distributed area scheduling node is extracted, and non-core tasks are filtered out. The global scheduling center filters out distributed regional scheduling nodes with CPU utilization below a preset value from all distributed regional scheduling nodes, and migrates the data packets of the filtered non-core tasks to the distributed regional scheduling nodes with CPU utilization below the preset value via Ethernet. Once the task migration is complete, the task allocation information of all distributed regional scheduling nodes is integrated through the global scheduling center to generate a regional task allocation plan that includes the number of AGV tasks allocated to each region, the task type, and the expected start time.

7. The method for optimizing and scheduling the automatic seeding path of a multi-layered automated warehouse according to claim 1, characterized in that, The specific process of local obstacle avoidance optimization is as follows: Collect obstacle distance data, ground image data, and moving target data to generate multi-source perception data, and form a dynamic environment map through local path planning units; Based on the regional task allocation plan and dynamic environment map, overlay analysis is performed through the collision detection module; If a collision risk is detected, the detour distance is calculated. If the detour distance is less than the preset value, the AGV parameters are adjusted using a dynamic window method combined with reinforcement learning, and the optimized AGV parameters are output. If the detour distance exceeds the preset value, the position and movement parameters of the obstacle will be fed back to the global scheduling center, triggering incremental replanning and forming an optimized AGV path list; By integrating and optimizing the AGV parameters and AGV path list through local path planning units, a task allocation table is generated that includes the optimized path and estimated arrival time of each AGV.

8. The method for optimizing and scheduling the automatic seeding path of a multi-layered automated warehouse according to claim 1, characterized in that, The calculation process for the task priority is as follows: Based on the task allocation table, extract the order urgency, material characteristics, and remaining time for each task, and substitute them into the task priority calculation formula to calculate the task priority, as follows: ； in, This represents the task priority value. The larger the value, the higher the task priority. The urgency level of the order. For material sensitivity coefficient, This represents the risk factor for task timeout.

9. The method for optimizing and scheduling the automatic seeding path of a multi-layered automated warehouse according to claim 8, characterized in that, The specific process for outputting the final task scheduling instruction is as follows: Based on the task priorities and task allocation table of all tasks, resources matching the path attributes are allocated to each task in descending order of priority, and a preliminary resource allocation scheme is output. Based on the initial resource allocation scheme, compare the optimized paths of all tasks. If the AGV path of a low-priority task intersects with the AGV path of a high-priority task and the time overlaps, schedule the low-priority AGV to stop at the nearest avoidance zone and continue driving after the high-priority task has passed. Output the updated resource allocation scheme. Based on the task priority of all tasks, bind a spare AGV to the task with a priority greater than the preset value, and output a redundancy configuration table; The initial resource allocation scheme, task allocation table, and redundancy configuration table are integrated into a structured final task scheduling instruction.

10. A multi-layer automated seeding path optimization and scheduling system for automated storage and retrieval systems, characterized in that: include: The data interaction module includes multi-protocol interfaces, multi-source data acquisition units, data association units, and a real-time database. It achieves data transmission through 5G industrial Ethernet, providing basic data support for the entire system. The path planning module includes a global path planning unit and a local path planning unit. It interacts with the data interaction module in real time and outputs the initial path, regional task allocation plan, and optimized path list. The task scheduling module includes a priority calculation unit, a resource allocation unit, and a redundancy configuration unit. It receives the output data from the path planning module and outputs the final task scheduling instruction to the automatic seeding execution module. The automatic seeding execution module includes an intelligent AGV, a modular end effector, and a vision recognition unit. It receives instructions from the task scheduling module and completes material grabbing, transfer, and delivery. The environmental monitoring and fault self-diagnosis module includes an environmental data monitoring unit, a hardware fault detection unit, a backup resource scheduling unit, and a maintenance work order generation unit, which handles anomalies and faults.

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