Intelligent stereoscopic warehouse and operation method thereof

Abstract

The invention relates to the technical field of logistics, in particular to an intelligent stereoscopic warehouse and an operation method thereof. The method comprises the following steps: carrying out logistics tracking data acquisition on goods to be warehoused to obtain a current logistics tracking data set; according to the current logistics tracking data set, goods to be warehoused are classified, a goods classification list is obtained, and the goods classification list comprises a plurality of site fragile goods lists and a plurality of site non-fragile goods lists; according to the goods classification list, carrying out storage interval distribution on the to-be-warehoused goods to obtain a goods storage interval distribution list; according to the cargo storage interval distribution list, carrying out storage location distribution on the to-be-warehoused cargo to obtain a cargo storage location mapping table; and performing logistics updating on the current logistics tracking data set according to the cargo storage position mapping table to obtain an updated logistics tracking data set. The space utilization rate and the storage efficiency of the intelligent stereoscopic warehouse are improved, and the space pressure of a logistics center is effectively relieved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics, and in particular to an intelligent stereoscopic warehouse and an operation method thereof. Background Art

[0002] In the modern logistics industry, with the rapid development of e-commerce, logistics centers are facing tremendous pressure to handle goods. Traditional flat warehouses have been unable to meet the growing storage and sorting needs due to problems such as low space utilization and low efficiency in cargo storage and retrieval. In addition, manually operated warehouses have a high risk of damage when handling fragile or special goods, which not only increases logistics costs but also affects customer satisfaction. In order to improve the storage efficiency and automation level of warehouses, the concept of stereoscopic warehouses came into being. Stereoscopic warehouses can significantly improve the storage capacity per unit area by utilizing vertical space. However, the existing stereoscopic warehouse system still has some challenges in actual operation, such as inaccurate cargo classification, unreasonable storage interval allocation, and unscientific cargo storage and retrieval path planning, which limit the operating efficiency and accuracy of stereoscopic warehouses.

[0003] In terms of outbound operations, stereoscopic warehouses with low automation levels often rely on a large amount of manual operations, which not only increases labor costs, but also makes it difficult to ensure the timeliness and accuracy of outbound operations. At the same time, for the management of stranded goods, traditional methods often lack effective analysis and scheduling mechanisms, resulting in goods being stranded in warehouses for too long, affecting the overall flow speed of logistics. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide an intelligent high-bay warehouse and an operation method thereof to solve at least one of the above technical problems.

[0005] To achieve the above purpose, an intelligent high-bay warehouse operation method comprises the following steps:

[0006] Step S1: Collect logistics tracking data of the incoming goods to obtain a current logistics tracking data set; classify the incoming goods according to the current logistics tracking data set to obtain a goods classification list, wherein the goods classification list includes a list of fragile goods at several sites and a list of non-fragile goods at several sites;

[0007] Step S2: allocating storage intervals for incoming goods according to the goods classification list to obtain a goods storage interval allocation list; allocating storage locations for incoming goods according to the goods storage interval allocation list to obtain a goods storage location mapping table;

[0008] Step S3: perform logistics update on the current logistics tracking data set according to the cargo storage location mapping table to obtain an updated logistics tracking data set, and synchronize the updated logistics tracking data set to the preset logistics monitoring platform in real time; determine the handling plan for the incoming cargo according to the cargo storage location mapping table to obtain the cargo incoming handling plan; perform cargo directional transportation operations on the incoming cargo according to the cargo storage location mapping table and the cargo incoming handling plan;

[0009] Step S4: collecting outbound demand for the intelligent stereoscopic warehouse to obtain an outbound cargo list; determining an outbound cargo plan for the stored cargo according to the cargo storage location mapping table and the outbound cargo list to obtain an outbound cargo execution plan; performing automated outbound operations on the stored cargo according to the outbound cargo execution plan to obtain a storage state matrix of the third warehouse;

[0010] Step S5: Cargo detention analysis is performed on the intelligent stereoscopic warehouse according to the storage status matrix of the third warehouse to obtain a cargo detention status report; a scheduling plan is determined for the intelligent stereoscopic warehouse according to the cargo detention status report to obtain an intelligent scheduling plan for the stranded goods, and the stranded goods are processed for outbound delivery in batches according to the intelligent scheduling plan for the stranded goods.

[0011] The present invention greatly improves the space utilization and storage efficiency by adopting the stereoscopic warehouse design, and effectively alleviates the space pressure of the logistics center. By collecting and classifying the logistics tracking data of the incoming goods in detail, the refined storage interval allocation of the goods is realized, the damage risk of fragile and non-fragile goods is reduced, and the access efficiency is improved. At the same time, by updating and synchronizing the logistics tracking data to the monitoring platform in real time, the transparency and customer satisfaction of logistics management are enhanced. Through the automated outbound operation, the manual dependence is reduced, the operation efficiency and accuracy are improved, and the operational efficiency and market responsiveness of the logistics center are enhanced. Through analysis and scheduling, the retention time of goods is shortened, the logistics circulation is accelerated, and the inventory cost is reduced. In addition, the adaptability and flexibility of the system are enhanced, and the strategy can be dynamically adjusted according to the real-time data to better cope with the peak cargo flow and diversified storage requirements. In summary, the present invention significantly improves the efficiency and accuracy of logistics operations, reduces cargo damage, and accelerates the circulation of goods, thereby improving the overall logistics service quality and meeting the market demand for efficient and reliable logistics services.

[0012] Preferably, the present invention further provides an intelligent stereoscopic warehouse, comprising an intelligent control system, wherein the intelligent control system is used to execute the intelligent stereoscopic warehouse operation method as described above, and the intelligent stereoscopic warehouse comprises:

[0013] The cargo classification module is used to collect logistics tracking data of incoming cargo to obtain the current logistics tracking data set; classify incoming cargo according to the current logistics tracking data set to obtain a cargo classification list, wherein the cargo classification list includes a list of fragile cargo at several sites and a list of non-fragile cargo at several sites;

[0014] An allocation module is used to allocate storage intervals for incoming goods according to the goods classification list to obtain a goods storage interval allocation list; allocate storage locations for incoming goods according to the goods storage interval allocation list to obtain a goods storage location mapping table;

[0015] The cargo storage module is used to perform logistics updates on the current logistics tracking data set according to the cargo storage location mapping table, obtain an updated logistics tracking data set, and synchronize the updated logistics tracking data set to the preset logistics monitoring platform in real time; determine the handling plan for the incoming cargo according to the cargo storage location mapping table, and obtain the cargo incoming handling plan; perform cargo directional transportation operations on the incoming cargo according to the cargo storage location mapping table and the cargo incoming handling plan;

[0016] The outbound execution module is used to collect outbound demand of the intelligent stereoscopic warehouse and obtain an outbound goods list; determine the outbound plan for the stored goods according to the goods storage location mapping table and the outbound goods list to obtain an outbound execution plan; perform automated outbound operations on the stored goods according to the outbound execution plan to obtain a storage state matrix of the third warehouse;

[0017] The detention scheduling module is used to analyze the cargo detention of the intelligent stereoscopic warehouse according to the storage status matrix of the third warehouse to obtain a cargo detention status report; determine the scheduling plan for the intelligent stereoscopic warehouse according to the cargo detention status report to obtain an intelligent scheduling plan for the stranded cargo, and perform batch outbound processing of the stranded cargo according to the intelligent scheduling plan for the stranded cargo.

[0018] In the present invention, through modular design, the accuracy and efficiency of the operation are significantly improved, the rapid and accurate classification and proper handling of incoming goods are realized, and the risk of damage is reduced. The intelligent allocation module in the system optimizes the utilization of storage space, improves storage density, and effectively reduces storage costs. At the same time, the cargo storage module ensures the real-time update and transparency of logistics information and enhances the controllability of management. The automated outbound operation of the outbound execution module speeds up the flow of goods, shortens the residence time, improves customer satisfaction, and reduces additional costs. The detention scheduling module effectively manages and handles stranded goods in a timely manner through intelligent analysis, thereby improving the warehouse's response speed and service quality. The entire system reduces dependence on manual labor, reduces labor costs, reduces resource waste through optimization strategies, and further reduces operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0020] Figure 1 A schematic flow chart of the steps of an intelligent stereoscopic warehouse operation method according to an embodiment is shown.

[0021] Figure 2 A detailed step flow diagram of step S278 of an embodiment is shown.

[0022] Figure 3 A detailed flow chart of step S3 of an embodiment is shown. DETAILED DESCRIPTION

[0023] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0025] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 3 The present invention provides an intelligent stereoscopic warehouse operation method, comprising the following steps:

[0027] Step S1: Collect logistics tracking data of the incoming goods to obtain a current logistics tracking data set; classify the incoming goods according to the current logistics tracking data set to obtain a goods classification list, wherein the goods classification list includes a list of fragile goods at several sites and a list of non-fragile goods at several sites;

[0028] Step S2: allocating storage intervals for incoming goods according to the goods classification list to obtain a goods storage interval allocation list; allocating storage locations for incoming goods according to the goods storage interval allocation list to obtain a goods storage location mapping table;

[0029] Step S3: perform logistics update on the current logistics tracking data set according to the cargo storage location mapping table to obtain an updated logistics tracking data set, and synchronize the updated logistics tracking data set to the preset logistics monitoring platform in real time; determine the handling plan for the incoming cargo according to the cargo storage location mapping table to obtain the cargo incoming handling plan; perform cargo directional transportation operations on the incoming cargo according to the cargo storage location mapping table and the cargo incoming handling plan;

[0030] Step S4: collecting outbound demand for the intelligent stereoscopic warehouse to obtain an outbound cargo list; determining an outbound cargo plan for the stored cargo according to the cargo storage location mapping table and the outbound cargo list to obtain an outbound cargo execution plan; performing automated outbound operations on the stored cargo according to the outbound cargo execution plan to obtain a storage state matrix of the third warehouse;

[0031] Step S5: Cargo detention analysis is performed on the intelligent stereoscopic warehouse according to the storage status matrix of the third warehouse to obtain a cargo detention status report; a scheduling plan is determined for the intelligent stereoscopic warehouse according to the cargo detention status report to obtain an intelligent scheduling plan for the stranded goods, and the stranded goods are processed for outbound delivery in batches according to the intelligent scheduling plan for the stranded goods.

[0032] In this embodiment, first, the logistics tracking data of the incoming goods are collected by using the automatic scanning system installed at the warehouse entrance, such as RFID readers and barcode scanners. These devices automatically read the labels on each cargo package and transmit the data to the warehouse management system (WMS) to form the current logistics tracking data set. Next, the logistics tracking data set is analyzed, and the goods are divided into fragile and non-fragile categories according to the fragility and other attributes of the goods, and a fragile goods list and a non-fragile goods list are generated for each station, which are integrated into a cargo classification list. Then, the three-dimensional simulation model of the warehouse is used to evaluate the storage space, and the storage interval allocation is performed in the warehouse control system (WCS) using a genetic algorithm or a simulated annealing algorithm in combination with the cargo classification list to generate a cargo storage interval allocation list. These algorithms consider the size, weight and fragility of the goods, as well as the capacity and location of the storage interval to optimize the use of storage space. Based on the cargo storage interval allocation list, the WCS further instructs the automated storage and retrieval system (AS / RS) to allocate specific storage locations for each piece of cargo and create a cargo storage location mapping table. This mapping table is synchronized with the real-time database of the warehouse to ensure the accuracy and real-time nature of the data. Subsequently, WMS updates the logistics tracking data set according to the updated cargo storage location mapping table, and uses the API interface to synchronize the data in real time to the preset logistics monitoring platform, such as the supply chain management (SCM) system or the customer relationship management (CRM) system, so that all relevant parties can track the status of the cargo in real time. Using the path planning tool in WCS, the cargo inbound handling plan is determined according to the cargo storage location mapping table and the predetermined handling rules. This plan includes the handling path, the equipment used, and the estimated time to ensure that the cargo can be transported to the designated cargo location efficiently and safely. During the handling process, the automated guided vehicle (AGV) or the robot arm performs the cargo directional transportation operation according to the handling plan, and moves the cargo from the receiving area to the designated storage location. In the outbound stage, WMS collects outbound demand and generates an outbound cargo list through integration with the sales system and order management system. Then, combined with the cargo storage location mapping table and the outbound cargo list, WCS uses constraint satisfaction algorithm or integer programming algorithm to determine the outbound execution plan for the cargo, including the outbound order and path. Finally, the automated outbound equipment executes the automated outbound operation according to the outbound execution plan, and the WMS updates the warehouse storage status matrix and generates the third warehouse storage status matrix. Using data mining technology, the WMS analyzes the third warehouse storage status matrix and generates a cargo detention status report. Based on the detention status report, the WMS uses optimization algorithms, such as linear programming or heuristic algorithms, to develop an intelligent scheduling plan for the detained cargo and guide the batch outbound processing of the detained cargo.

[0033] Preferably, step S1 comprises the following steps:

[0034] Step S11: Collect logistics tracking data for the incoming goods to obtain a current logistics tracking data set;

[0035] Specifically, it can be connected with the logistics company's information system to automatically obtain the logistics tracking data of the goods that are about to enter the warehouse. This data includes the goods' shipping order number, estimated arrival time, sender and recipient information, etc. By scanning the barcode or RFID tag on the goods packaging, this information is associated with the goods entity to form the current logistics tracking data set. Data collection can be completed through an automated scanning station at the warehouse entrance, which is equipped with a high-speed scanner and data reading equipment, which can quickly and accurately identify and record the logistics information of each incoming goods.

[0036] Step S12: parsing the cargo information of the current logistics tracking data set to obtain a cargo attribute data set, wherein the cargo attribute data set includes a plurality of cargo attribute data, each cargo attribute data at least including cargo ID data, cargo size data, cargo weight data, cargo next station data and cargo fragility data;

[0037] Specifically, the specific attributes of each item can be extracted from the logistics tracking data set, including the item ID, size, weight, next stop, and fragility. For example, the item ID is "100001", the size is "100cm x 50cm x30cm", the weight is "20kg", the next stop is "distribution center A", and the fragility is marked as "fragile". This information is organized into a structured item attribute data set.

[0038] Step S13: clustering the incoming goods into sites according to the next site data of the goods, and obtaining a list of goods at several sites;

[0039] Specifically, all goods destined for "distribution center A" can be classified into one category according to the "goods next station data" in the goods attribute data set, and all goods destined for "distribution center B" can be classified into another category, and so on. Such clustering operations can be automatically completed by the classification algorithm in the warehouse management system. The clustering results form several station cargo lists, each of which contains all the cargo information sent to the same station.

[0040] Step S14: Classify the fragility of the site cargo list according to the cargo fragility data to obtain a site fragile cargo list and a site non-fragile cargo list;

[0041] Specifically, fragile and non-fragile goods can be distinguished by analyzing the fragility data of the goods. For example, goods marked as "fragile", such as glass products or porcelain, are automatically classified into the "site fragile goods list". Conversely, goods without fragility marks or marked as "non-fragile", such as plastic products or metal tools, are classified into the "site non-fragile goods list". This classification process can be automatically completed through conditional statements set in database queries to ensure that each piece of goods can be correctly classified according to its fragility.

[0042] Step S15: After traversing the cargo lists of several sites, execute step S14 to obtain the fragile cargo lists of several sites and the non-fragile cargo lists of several sites, and record the fragile cargo lists of several sites and the non-fragile cargo lists of several sites as cargo classification lists.

[0043] Specifically, the cargo lists of all sites can be traversed, and the classification operation in step S14 can be performed. The cargo lists of each site are processed in turn, and the fragility of the goods in each list is classified to generate a fragile cargo list and a non-fragile cargo list of the site. For example, for the cargo list of "distribution center A", a "distribution center A fragile cargo list" and a "distribution center A non-fragile cargo list" will be generated. This process will be repeated until the cargo lists of all sites have been processed. Finally, the fragile cargo lists and non-fragile cargo lists of all sites are summarized to form a comprehensive cargo classification list.

[0044] The present invention greatly enhances the accuracy of cargo management by collecting and analyzing cargo logistics tracking data, and provides detailed data support for warehouse operations. This in-depth analysis can efficiently and accurately classify cargo, and intelligently arrange storage according to the next station and fragility of the cargo, which not only improves operational efficiency but also ensures storage safety. In addition, this strategy optimizes the storage location of cargo, reduces the loss of fragile cargo during storage, and at the same time improves the automation level of logistics operations, reduces manual intervention, and reduces costs caused by misclassification or improper storage.

[0045] Preferably, step S2 comprises the following steps:

[0046] Step S21: performing a three-dimensional spatial scan on the intelligent stereoscopic warehouse to obtain a three-dimensional model of the warehouse, and performing cargo location recognition on the three-dimensional model of the warehouse to obtain an initial cargo location layout data set;

[0047] Specifically, a 3D laser scanner can be used to perform a comprehensive spatial scan of the intelligent stereoscopic warehouse. The scanner is installed in multiple locations in the warehouse, or a mobile scanning device is used to scan to ensure that every corner of the warehouse is covered. Through scanning, detailed 3D data of the warehouse can be obtained, including the size of the shelves, layer height, aisle width, etc. These data are used to build an accurate 3D model of the warehouse, which is presented in computer-aided design (CAD) software. Subsequently, the location and characteristics of each cargo space are automatically identified and marked through an image recognition algorithm to form an initial cargo space layout data set.

[0048] Step S22: dividing the initial cargo location layout data set into storage intervals to obtain a storage interval division data set, wherein the storage interval division data set includes a plurality of storage intervals;

[0049] Specifically, the storage space of the warehouse can be logically divided into intervals based on the initial cargo layout dataset. Considering factors such as the spatial structure of the warehouse, the size and weight restrictions of the goods, and the convenience of access, the warehouse is divided into several logical storage intervals. For example, according to the arrangement of shelves and the layout of aisles, the warehouse is divided into different storage intervals such as "Zone 1" and "Zone 2". Each storage interval is assigned a unique identifier and recorded in the system. After the division is completed, a storage interval division dataset is generated, which records the location, size, and characteristics of each storage interval in detail.

[0050] Step S23: Based on the storage interval division data set, the space capacity and available cargo space statistics of the intelligent stereoscopic warehouse are performed to obtain a first warehouse storage state matrix, wherein the first warehouse storage state matrix includes the space capacity data and available cargo space data of each storage interval;

[0051] Specifically, the data set can be divided into storage intervals to perform statistics on the space capacity and available cargo space of each storage interval in the intelligent stereoscopic warehouse. The total volume or area of ​​each storage interval is calculated, and the number and type of goods that can be accommodated in each interval are estimated based on the size and shape of the cargo space. After the statistics are completed, the first warehouse storage status matrix is ​​generated, which records the space capacity data and current available cargo space data of each storage interval in detail.

[0052] Step S24: collecting distribution plan data for the cargo classification list based on the current logistics tracking data set to obtain a site distribution plan data set;

[0053] Specifically, the cargo flow information in the logistics tracking dataset can be analyzed, including the estimated arrival time, destination, and urgency of the cargo. Then, based on this information, combined with historical delivery data and real-time traffic conditions, a delivery plan is generated for each site. This plan details the type, quantity, and time window of cargo that needs to be delivered at each site. After completing these operations, a site delivery plan dataset will be generated.

[0054] Step S25: according to the site distribution plan data set, the outbound time of the goods to be stored corresponding to the site fragile goods list and the site non-fragile goods list is estimated to obtain the outbound time prediction data of the goods at the same site, wherein the site fragile goods list and the site non-fragile goods list belong to the same goods list of the next site;

[0055] Specifically, the site distribution plan dataset can be used to estimate the outbound time of fragile and non-fragile goods at each site. The time window in the distribution plan and the specific attributes of the goods, such as fragility, are analyzed to predict the outbound order and time of each batch of goods. For example, a batch of fragile glass products is predicted to be outbound in three days, while non-fragile metal parts are predicted to be outbound in a week. These prediction data will help warehouse managers prepare for the picking and packing of goods in advance to ensure on-time delivery. The system also considers the storage location and transportation path of the goods to optimize the outbound process. Finally, the outbound time prediction data of the goods at the same site is generated.

[0056] Step S26: After traversing the fragile goods lists of several stations and the non-fragile goods lists of several stations in the cargo classification list, execute step S25 to obtain the forecast data of the outbound time of several cargoes at the same station;

[0057] Specifically, the fragile goods lists and non-fragile goods lists of all sites can be traversed, and the outbound time estimation in step S25 can be performed for the goods in each list. Each list is processed in turn, and the outbound time of each batch of goods is estimated according to the attributes of the goods and the distribution plan. An efficient prediction algorithm, such as a regression model in time series analysis or machine learning, is used to process the goods data in each list. For example, a linear regression model is used, which predicts the outbound time based on historical outbound data, goods type, order priority, and current warehouse traffic. For each goods, its attribute data (such as size, weight, and fragility mark) and distribution plan information (such as order deadline and customer requirements) are input into the model. The algorithm will analyze this data, considering the specific attributes of the goods and the historical outbound mode, to predict the outbound time of each batch of goods. Once the goods in all lists are processed, the outbound time prediction data of the goods at each site will be summarized to form a comprehensive outbound time prediction data set. This data set will include the outbound number, storage location, predicted outbound time and related attributes of each goods. For example, the fragile goods with the predicted shipment ID "100002" are expected to be shipped from the third shelf in area C of the warehouse in three days.

[0058] Step S27: Allocate storage intervals for the incoming goods according to the first warehouse storage status matrix, the cargo classification list and the cargo outbound time prediction data of the same site to obtain a cargo storage interval allocation list, and allocate storage locations for the incoming goods according to the cargo storage interval allocation list to obtain a cargo storage location mapping table.

[0059] Specifically, please refer to the sub-steps of step S27 for the detailed implementation process of this embodiment.

[0060] The present invention realizes efficient utilization and precise management of warehouse space through three-dimensional space scanning and modeling. This process not only optimizes the division of storage intervals and improves space utilization, but also provides real-time data support for cargo storage and handling through dynamic storage status management. By combining logistics tracking and distribution planning, it is possible to intelligently predict the time of cargo departure, optimize the distribution process, and reduce the waste of space and resources. Intelligent storage interval allocation and precise storage location mapping further improve operational efficiency and ensure fast and accurate storage and retrieval of cargo. In addition, the risk of loss is effectively reduced by careful classification and proper placement of fragile and non-fragile cargo.

[0061] Preferably, step S27 includes the following steps:

[0062] Step S271: Based on the warehouse three-dimensional model, the data set is divided according to the storage intervals to perform structural stability evaluation on each storage interval, and obtain an interval structural stability data set;

[0063] Specifically, the structural stability of each storage interval can be evaluated using a warehouse 3D model and a storage interval partitioning dataset. First, the physical properties of each storage interval in the warehouse 3D model are analyzed, including the material of the shelves, their load-bearing capacity, and how they are connected to other warehouse structures. Then, the structural stability of each interval is scored based on building codes and safety standards. This score takes into account factors that affect structural stability, such as seismic activity, shelf overload, and environmental changes. Historical maintenance records and any known structural problems can also be considered. After the assessment is completed, an interval structural stability dataset will be generated, which details the structural stability score and related safety recommendations for each storage interval.

[0064] Step S272: performing vibration sensitivity analysis on each storage interval according to the storage interval division data set based on the warehouse three-dimensional model to obtain an interval vibration sensitivity data set;

[0065] Specifically, the dataset can be divided based on the warehouse's 3D model and storage compartments, taking into account vibration sources caused by the warehouse's internal and external sources, such as nearby traffic flow, industrial machinery operation, or natural phenomena. The response of each storage compartment under different vibration conditions is simulated to evaluate its sensitivity to vibration. For example, some areas are found to be more sensitive due to their proximity to large machinery or busy roads. After the analysis is completed, an interval vibration sensitivity dataset will be generated, recording the vibration sensitivity score of each interval.

[0066] Step S273: collecting the current location data of the goods to be stored, and obtaining the current location data of the goods;

[0067] Specifically, the current location data of the goods to be stored can be collected. This can be done through a positioning system in the warehouse, such as an RFID reader, a barcode scanner, or a visual recognition system. When the goods arrive at the warehouse entrance, the identification on the goods, such as a barcode or RFID tag, is automatically scanned to determine the identity of the goods and the expected storage location. In addition, a sensor network in the warehouse can be used to track the movement of goods in the warehouse. These sensors can be weighing sensors on the ground, proximity sensors on the shelves, or GPS locators on transport vehicles. Through these technologies, the location information of the goods can be updated in real time, generating a detailed current location data of the goods.

[0068] Step S274: performing access path statistics of the incoming goods and the storage intervals based on the warehouse three-dimensional model and the current location data of the goods, and obtaining a goods access path data set;

[0069] Specifically, the current location of each piece of cargo can be identified in the 3D warehouse model, and then the optimal access path can be planned based on the size, weight and fragility of the cargo, as well as the location and characteristics of the storage compartments. These paths take into account the traffic flow in the warehouse, the access restrictions of the shelves, and the requirements of safe passages. Use a path planning algorithm, such as Dijkstra or A* algorithm, to determine the shortest or most efficient path. After the calculation is completed, a cargo access path dataset will be generated, which details the recommended path for each piece of cargo to each storage compartment.

[0070] Step S275: Based on the storage interval division data set, the storage interval is demarcated as a seismic priority zone according to the interval structural stability data set and the interval vibration sensitivity data set to obtain seismic priority zone data, and the storage interval division data set is removed from the seismic priority zone according to the seismic priority zone data to obtain non-fragile goods priority zone data;

[0071] Specifically, the seismic priority areas can be delineated by combining the storage interval division data set, the interval structural stability data set, and the interval vibration sensitivity data set. First, the storage intervals with the most stable structure and the least sensitive to vibration are identified. These areas are suitable for storing fragile goods. Then, these intervals are marked as seismic priority areas and specially marked in the storage interval division data set. Next, based on the seismic priority area data, those areas that are not suitable for storing fragile goods, especially those with poor structural stability or more sensitive to vibration, will be eliminated from other storage intervals. This process will generate seismic priority area data and non-fragile goods priority area data.

[0072] Step S276: obtaining a cargo attribute data set, performing storage interval demand statistics on fragile cargo according to the cargo attribute data set and the fragile cargo list of the site, obtaining fragile cargo storage demand data, and allocating storage intervals for fragile cargo according to the fragile cargo storage demand data and the earthquake-proof priority zone data, obtaining a fragile cargo storage interval allocation table;

[0073] Specifically, a cargo attribute data set can be obtained and combined with the fragile cargo list of the site to count the storage interval requirements for fragile cargo. The attributes of each fragile cargo, such as size, weight, and fragility level, as well as the data of the earthquake priority zone, are analyzed to determine the most suitable storage interval for each cargo. Optimization algorithms, such as linear programming or integer programming, are used to allocate storage intervals to ensure that fragile cargo can be stored in the safest location. At the same time, the cargo outbound time forecast and distribution plan can also be considered to reduce the distance and time the cargo moves in the warehouse. After the allocation is completed, a fragile cargo storage interval allocation table will be generated, which records in detail the storage location of each fragile cargo and related storage recommendations.

[0074] Step S277: performing storage interval demand statistics on non-fragile goods according to the goods attribute data set and the non-fragile goods list of the site to obtain non-fragile goods storage demand data, and allocating storage intervals for non-fragile goods according to the goods access path data set and the non-fragile goods storage demand data based on the non-fragile goods priority area data to obtain a non-fragile goods storage interval allocation table, and recording the fragile goods storage interval allocation table and the non-fragile goods storage interval allocation table as a goods storage interval allocation list;

[0075] Specifically, the attributes of non-fragile goods such as size, weight, and type can be analyzed with reference to the cargo attribute dataset and the non-fragile cargo list of the site to determine the storage requirements of each piece of cargo. For example, a batch of non-fragile goods is identified to be mainly composed of large mechanical equipment and small plastic products, which have different storage space requirements. Next, the non-fragile cargo priority area data is used, which indicates the areas in the warehouse that are suitable for storing non-fragile goods. These areas are selected because of their high structural stability or low vibration impact. The cargo access path dataset can also be considered. This dataset contains the optimal access path for each piece of cargo to each storage interval to ensure fast access to the goods. Combined with this information, an optimization algorithm, such as a genetic algorithm or a simulated annealing algorithm, is used to assign the most appropriate storage interval to each non-fragile cargo. After the storage interval allocation of all non-fragile goods is completed, a non-fragile cargo storage interval allocation table will be generated, which records the storage location and related storage parameters of each non-fragile cargo in detail. Finally, the fragile cargo storage interval allocation table and the non-fragile cargo storage interval allocation table are merged to form a complete cargo storage interval allocation list.

[0076] Step S278: Allocate storage locations for incoming goods according to the goods storage interval allocation list, the goods classification list and the goods attribute data set to obtain a goods storage location mapping table.

[0077] Specifically, please refer to the sub-steps of step S278 for the detailed implementation process of this embodiment.

[0078] The present invention significantly improves the safety of storage intervals by comprehensively using structural stability assessment and vibration sensitivity analysis, and effectively reduces the potential damage to goods caused by structural problems and vibration. At the same time, by optimizing the access path of goods, a more efficient and shorter transportation route can be planned, reducing equipment energy consumption and operation time, thereby improving overall operation efficiency. The intelligent storage interval allocation strategy ensures that fragile and non-fragile goods can be reasonably placed according to their characteristics, further enhancing the rationality and efficiency of storage. In addition, the generated accurate storage location mapping table not only speeds up the access speed of goods, but also improves the accuracy of operations. Through detailed storage interval demand statistics and allocation, the warehouse space is more fully utilized, improving space utilization. Through reasonable storage strategies and the demarcation of earthquake-proof priority areas, the loss of goods during storage and transportation is reduced, and the integrity of goods is guaranteed. The intelligent design of the present invention provides accurate operating instructions for automated equipment, reduces the need for manual operation, and improves the automation level of warehouse operations.

[0079] Preferably, step S278 includes the following steps:

[0080] Step S2781: Based on the warehouse three-dimensional model, the storage locations in the cargo storage area allocation list are identified in height, and a storage area location height mapping table is obtained;

[0081] Specifically, all available storage locations can be identified in the warehouse 3D model and the exact height of each location can be measured. This data includes the level of the location, the vertical distance from the ground, and the physical location of the location. Then, this height information is associated with each storage interval in the cargo storage interval allocation list to create a storage interval location height mapping table. This mapping table records the height information of each location in each storage interval in detail.

[0082] Step S2782: sorting the fragile goods list and the non-fragile goods list of the site in descending order according to the goods weight according to the goods attribute data set, to obtain a fragile goods weight sorting table and a non-fragile goods weight sorting table;

[0083] Specifically, the fragile cargo list and the non-fragile cargo list of the site can be processed according to the cargo attribute data set, and sorted in descending order according to the weight of the cargo. First, the weight information of each cargo is extracted from the cargo attribute data set, and then a sorting algorithm, such as quick sort or merge sort, is used to sort the fragile cargo and the non-fragile cargo from large to small according to weight. After the sorting is completed, two weight sorting tables will be generated: a fragile cargo weight sorting table and a non-fragile cargo weight sorting table. These tables will be used to guide subsequent storage allocation, ensuring that heavy cargo is preferentially allocated to storage areas with high structural stability, while light cargo can be allocated to higher or farther cargo locations.

[0084] Step S2783: According to the storage interval cargo height mapping table, the cargo storage interval allocation list, the fragile cargo weight sorting table and the non-fragile cargo weight sorting table, high priority storage allocation is performed on the corresponding cargo to be stored, and a cargo storage location mapping table is obtained.

[0085] Specifically, the goods in the same storage interval can be sorted from large to small according to their weight, and stored in the corresponding storage locations from low to high. This process begins with a detailed analysis of the storage interval storage location height mapping table, which provides the vertical height information of all storage locations in each storage interval. First, the data of the goods to be stored is obtained from the storage interval allocation list of the goods, which has assigned the corresponding storage intervals according to the classification and characteristics of the goods. Next, the weight sorting table of fragile goods and the weight sorting table of non-fragile goods are referred to. These two tables list the weights of all fragile and non-fragile goods in the same storage interval and sort them in order from heavy to light. Then, according to the storage location height mapping table of each storage interval, the allocation starts from the heaviest goods. The heaviest goods are allocated to the lowest available storage location in the same storage interval, and so on. The second heaviest goods are allocated to the second lowest storage location, ensuring that the lighter goods are stored in higher storage locations. In the allocation process, the size, shape and handling requirements of the goods can also be considered to ensure that the storage of the goods is both safe and in compliance with the operating specifications. For example, special consideration will be given to particularly large or special goods, and even if they are not the heaviest, they will be allocated to lower cargo spaces to facilitate the operation of handling equipment. After all goods are allocated, a cargo storage location mapping table will be generated, which records the storage location of each piece of goods in detail. This mapping table includes cargo space number, storage interval, cargo space height, and corresponding cargo information.

[0086] The present invention optimizes the utilization of storage space and ensures the precise match between cargo storage requirements and warehouse space by highly identifying and mapping storage locations. The weight sorting strategy not only improves the efficiency of cargo storage and retrieval and reduces the reliance on heavy handling equipment, but also takes special consideration of the safety of fragile goods and reduces the risk of breakage. At the same time, by giving priority to allocating appropriate cargo location heights, it reduces the number of employees working in dangerous or inconvenient locations, effectively improving operational safety. In addition, the generated cargo storage location mapping table enhances the flexibility of cargo management and enables warehouses to quickly adapt to the storage requirements of different goods. Accurate storage location information also provides data support for automated storage and retrieval systems, promoting the development of automated logistics operations.

[0087] Preferably, step S3 comprises the following steps:

[0088] Step S31: real-time positioning of the incoming goods is performed to obtain a real-time goods positioning data set, and the real-time goods positioning data set is supplemented with a position according to a goods storage location mapping table to obtain a detailed real-time goods positioning data set;

[0089] Specifically, the integrated warehouse management system (WMS) and warehouse control system (WCS) can be used to locate the incoming goods in real time using RFID technology, barcode scanners, and cameras in the warehouse. Whenever the goods enter the warehouse, the RFID tag or barcode on the goods is automatically scanned to determine the exact location of the goods. These location data, together with other information about the goods, such as the goods ID and the time of entry, are entered into the real-time goods location data set in real time. Subsequently, the real-time goods location data set is supplemented according to the goods storage location mapping table. This mapping table contains the recommended storage location for each goods, and this information is matched and updated with the real-time collected location data to ensure that the storage location of each goods is up to date and accurate. After completing the location supplement, the system will obtain a detailed real-time goods location data set.

[0090] Step S32: updating the current logistics tracking data set according to the detailed real-time cargo location data set to obtain an updated logistics tracking data set, and synchronizing the updated logistics tracking data set to a preset logistics monitoring platform in real time;

[0091] Specifically, the detailed real-time cargo location data set can be used to update the current logistics tracking data set. The real-time updated cargo location information is compared with the records in the logistics tracking data set, and the changed records are updated. These updates include the new location of the cargo, the estimated storage time, or the actual storage status. After the update is completed, the updated logistics tracking data set is automatically synchronized to the preset logistics monitoring platform in real time. This platform can be a cloud-based logistics management system that allows warehouse managers, logistics partners, and customers to view the status and location of the cargo in real time.

[0092] Step S33: collecting transport resource parameters of the intelligent stereoscopic warehouse to obtain a transport vehicle resource data set, wherein the transport vehicle resource data set includes a plurality of transport vehicle resource data, each transport vehicle resource data at least includes a maximum load capacity, a carriage size data, energy reserve data, a drivable path data, a vehicle working status data and a vehicle identification code;

[0093] Specifically, the transport resource parameters of all transport vehicles in the warehouse can be collected. Through on-board sensors, manual input or automatic data interface, the key parameters of each transport vehicle are collected, including maximum load capacity, vehicle size data, energy reserve data, drivable path data, vehicle working status data and vehicle identification code. These data will be entered into the transport vehicle resource data set.

[0094] Step S34: screening the transport vehicle resource data set for availability according to the energy reserve data and the vehicle working status data to obtain an available transport vehicle data set;

[0095] Specifically, the transporter resource data set can be carefully screened to determine which transporters are available for the upcoming cargo handling task. First, the energy reserve data of each transporter is checked, which includes but is not limited to the current level of fuel, power or other power sources. Only those transporters with energy reserves above a preset threshold are considered to have sufficient energy to complete the transportation task. Next, the vehicle working status data is analyzed, which involves the vehicle's maintenance record, fault history and current technical condition. Check whether the vehicle has any known faults or performance problems, and whether these problems will affect the vehicle's operating ability. Only those vehicles that do not have any known faults and are in good maintenance status will be marked as available. Combining the energy reserve data and the vehicle working status data, the transporters that meet both conditions are automatically screened out. For example, if a transporter has 80% power, and the last maintenance was a week ago, and no faults were recorded, then the transporter will be selected as available. The screening process can be automatically completed in the system by setting specific algorithms or rules to ensure that only the most suitable transporters are assigned to the handling task. After the screening is completed, the system will generate an available transport vehicle dataset, which contains detailed information of all available transport vehicles, including their energy status, working status, maximum load capacity, vehicle size, drivable routes and vehicle identification codes.

[0096] Step S35: Determine a transportation plan for the incoming goods according to the available transport vehicle data set and the cargo storage location mapping table to obtain a cargo incoming transportation plan;

[0097] Specifically, please refer to the sub-steps of step S35 for the detailed implementation process of this embodiment.

[0098] Step S36: Carry out directional transportation of the cargo to be stored in accordance with the cargo storage location mapping table and the cargo storage and handling plan.

[0099] Specifically, the cargo storage location mapping table and cargo warehousing and handling plan can be used to guide the directional transportation operation of the cargo to be stored. The designated storage location of each cargo to be stored is obtained from the cargo storage location mapping table. This mapping table lists in detail the specific cargo location where each cargo should be stored, including the cargo location number, the storage interval where it is located, the height of the cargo location, and other information. Next, refer to the cargo warehousing and handling plan, which includes the handling path of each cargo, the transportation vehicle used, the estimated handling time, and the allocation of handling personnel. According to the weight, size, fragility and other characteristics of the cargo, as well as the height and location of the cargo location, the most suitable handling path and transportation vehicle are determined. For example, a transport vehicle with a maximum load capacity of 5 tons is arranged to start from the warehouse entrance and go to the loading area along the preset path to load a batch of mechanical equipment weighing 3 tons. This batch of equipment will be transported to the designated storage interval and placed on the pre-assigned cargo location. In addition, according to the personnel allocation information in the handling plan, the handling personnel can be guided to load, unload and handle the cargo. The movers will use appropriate handling equipment, such as forklifts, stackers or automated guided vehicles (AGVs), to perform the handling tasks according to the system's instructions.

[0100] The present invention significantly enhances the accuracy of cargo tracking and ensures the transparency and controllability of logistics management by applying real-time positioning technology and intelligent management measures. By updating and synchronizing the logistics tracking data set to the monitoring platform in real time, the real-time and accuracy of logistics information are greatly improved, and changes in the status of goods can be responded to quickly. Through comprehensive parameter collection and availability screening of transport vehicle resources, transportation resource management is optimized, transportation tasks are reasonably allocated, and resource utilization efficiency is improved. Through intelligent handling solutions combined with available transport vehicle data and cargo storage locations, the optimal handling path is determined for each batch of goods, reducing handling time and distance, thereby improving operational efficiency. In summary, the present invention effectively reduces the manpower and energy costs of cargo handling, improves operational safety, and enhances the system's adaptability and flexibility to different handling requirements and environmental changes, ultimately improving customer satisfaction with logistics services.

[0101] Preferably, step S35 includes the following steps:

[0102] Step S351: Evaluate the energy status of available transport vehicles according to the energy reserve data to obtain an energy status chart of the transport vehicles;

[0103] Specifically, the energy reserve data of all available transport vehicles can be analyzed to evaluate the energy status of each vehicle. The current power, fuel level, or other relevant energy indicators of each transport vehicle are collected automatically through on-board sensors and transmitted to the warehouse management system in real time. Then, the energy reserve of each vehicle is evaluated based on preset energy efficiency standards and historical energy consumption patterns. The evaluation results will be used to generate a transport vehicle energy status chart that intuitively shows the energy level of each vehicle and the expected operating time or distance.

[0104] Step S352: Classifying the available transport vehicles into transport types according to the maximum load mass, the vehicle compartment size data and the transport vehicle energy status chart, and obtaining a transport vehicle transport type classification table, wherein the transport vehicle transport type classification table includes a long-distance transport type and a short-distance transport type;

[0105] Specifically, available transport vehicles can be classified into handling types based on the maximum load mass of the transport vehicle, the compartment size data, and the energy status chart. The physical and energy characteristics of each vehicle are analyzed and assigned to the most suitable handling type. For example, vehicles with large load capacity, spacious compartment space, and sufficient energy are classified as long-distance handling types. These vehicles are suitable for carrying tasks with long distances and large amounts of cargo. Vehicles with small load capacity and relatively low energy reserves are classified as short-distance handling types, which are suitable for handling operations inside warehouses or within short distances. After the classification is completed, a transport vehicle handling type classification table is generated, which clearly records the handling type of each vehicle.

[0106] Step S353: Obtain the warehouse three-dimensional model and the current location data of the goods, and classify the goods to be stored in the warehouse by transportation type based on the warehouse three-dimensional model according to the goods storage location mapping table and the current location data of the goods, and obtain a cargo transportation type classification table, wherein the cargo transportation type classification table includes long-distance transportation type and short-distance transportation type;

[0107] Specifically, the warehouse 3D model and the current location data of the goods can be obtained. The warehouse 3D model can be used to analyze the layout of the warehouse and the storage location of the goods. Then, the cargo storage location mapping table and the current location data of the goods can be combined to evaluate the handling distance and handling difficulty of each batch of goods. Based on this information, the goods are divided into long-distance handling types and short-distance handling types. For example, goods that need to be transported from one end of the warehouse to the other, or goods that need to be transported outside the warehouse, are classified as long-distance handling types; and goods that only need to be moved a short distance inside the warehouse are classified as short-distance handling types. After the classification is completed, a cargo handling type classification table will be generated, which records in detail the handling type and recommended handling path for each batch of goods.

[0108] Step S354: matching the incoming goods with transport vehicles according to the cargo handling type classification table and the transport vehicle handling type classification table to obtain a transport vehicle matching result table;

[0109] Specifically, the cargo handling type classification table and the transport vehicle handling type classification table can be used to perform the matching between the transport vehicle and the goods to be stored. The handling type of each batch of goods to be stored is analyzed, which includes factors such as the weight, size and fragility of the goods, as well as the distance they need to be transported. Then, based on this information, the most suitable transport vehicle is selected from the transport vehicle handling type classification table. For example, for heavy machinery and equipment that needs to be transported over long distances, a vehicle with high carrying capacity and sufficient energy reserves will be matched; while for light goods that are transported over short distances, a flexible and energy-efficient vehicle will be matched. During the matching process, the current location of the vehicle, the estimated available time, and other real-time factors can also be considered to ensure the efficiency and practicality of the matching. After the matching is completed, a transport vehicle matching result table will be generated, which records in detail the matching of each batch of goods with the corresponding transport vehicle, including information such as the vehicle identification code, the estimated handling time, and the matching cargo location.

[0110] Step S355: Obtain a cargo attribute data set, and plan the transport batches of the cargo to be stored according to the cargo attribute data set, the maximum load mass, the carriage size data, the transport vehicle matching result table and the cargo storage location mapping table to obtain a transport batch planning table;

[0111] Specifically, a cargo attribute data set can be obtained, which includes detailed information about each piece of cargo, such as weight, size, fragility, etc. Combined with this data, as well as the maximum load-bearing mass of the transport vehicle, the compartment size data, and the transport vehicle matching result table, the transport batches of each batch of goods to be stored are planned. The carrying capacity of each vehicle and the attributes of the cargo are evaluated to determine the quantity and type of cargo that each vehicle can handle. In addition, the cargo storage location mapping table is referenced to ensure that the cargo is effectively allocated to the appropriate cargo location. Use optimization algorithms, such as linear programming or heuristic algorithms, to plan transport batches to minimize handling time and cost while ensuring the safety and efficiency of the handling operation. After the planning is completed, a transport batch planning table will be generated, which details the transport vehicles, estimated handling time, and cargo location allocation for each batch of cargo.

[0112] Step S356: Obtain the cargo access path data set, and determine the transportation path for the incoming cargo according to the cargo access path data set and the cargo storage location mapping table, obtain the transportation path data table, and record the transport batch planning table and the transportation path data table as the cargo incoming transportation plan.

[0113] Specifically, a cargo access path data set can be obtained, which includes the handling paths between various cargo locations in the warehouse. Combine these path data with the cargo storage location mapping table to determine the specific handling path for each batch of goods to be stored. Analyze the terrain, traffic conditions, and potential obstacles of each path to ensure that the selected path is both safe and efficient. The size and weight of the goods, as well as the type of handling equipment, can also be considered to optimize the path selection. For example, for heavy or large goods, the system selects a wider and more sturdy path to avoid potential damage. After the path is determined, a handling path data table will be generated, which details the handling path, estimated handling time, and related safety requirements for each batch of goods. Finally, the transport batch planning table and the handling path data table are integrated to form a complete cargo storage and handling plan.

[0114] The present invention achieves maximum energy utilization and optimization of vehicle use by comprehensively evaluating the energy status and physical characteristics of the transport vehicle, thereby improving energy efficiency and reducing waste. By carefully classifying the vehicles according to the handling type, the system can ensure that the transportation capacity of each vehicle is fully utilized. At the same time, by accurately matching cargo demand with vehicle capacity, the efficiency and accuracy of scheduling are further improved. By rationally planning transportation batches and routes, transportation costs are effectively reduced, while taking into account the characteristics of vehicles and cargo, the safety of operations is ensured. The flexibility of the system has been enhanced, and the operation plan can be quickly adjusted according to real-time data to adapt to changing needs. These measures have jointly improved the efficiency of cargo handling, shortened the handling time, and accelerated the warehousing speed of goods, thereby improving the overall logistics efficiency, and ensuring that goods can be stored in a timely and accurate manner, meeting the timeliness requirements of warehousing operations.

[0115] Preferably, step S4 comprises the following steps:

[0116] Step S41: obtaining a first warehouse storage state matrix, and synchronously updating the storage state of the first warehouse storage state matrix according to the cargo storage location mapping table to obtain a second warehouse storage state matrix;

[0117] Specifically, the first warehouse storage status matrix can be obtained, which contains real-time storage information of all storage intervals in the warehouse, such as used cargo spaces, available space, types of stored goods, etc. Then, the cargo storage location mapping table, which is a database that records the current storage locations of all goods, is used to update the first warehouse storage status matrix. Whenever new goods are put into or taken out of the warehouse, the mapping table and storage status matrix are automatically updated to reflect the latest storage situation. Through this synchronous update, the second warehouse storage status matrix is ​​generated.

[0118] Step S42: collecting outbound demand from the intelligent stereoscopic warehouse to obtain an outbound goods list;

[0119] Specifically, outbound orders can be received from the order management system. These orders can come from different customers and different sales channels. These orders are analyzed to extract the information of the goods that need to be shipped, including the ID, quantity, and time window of the goods. Then, a list of outbound goods is generated based on this information. This list lists all the goods that are about to be shipped in detail, and finally the outbound goods list is obtained.

[0120] Step S43: obtaining a three-dimensional warehouse model, and performing warehouse storage state mapping on the three-dimensional warehouse model according to the second warehouse storage state matrix to obtain a dynamic storage model of the warehouse;

[0121] Specifically, a 3D warehouse model can be obtained, which is a virtual and accurate representation of the warehouse layout, including all physical structures such as shelves, aisles, and cargo spaces. Then, the 3D model is updated according to the second warehouse storage status matrix, which contains the latest warehouse storage information. The used cargo spaces, available space, and stored goods are marked in the model to generate a dynamic storage model of the warehouse. This model not only reflects the physical structure of the warehouse, but also displays the storage status of the warehouse in real time.

[0122] Step S44: extracting the outbound target location from the outbound goods list to obtain the outbound target location data of the goods;

[0123] Specifically, the outbound goods list can be analyzed to extract the outbound target location of each piece of goods, which is actually the loading area where the goods arrive in the warehouse. First, the outbound demand of the goods is obtained from the order information, which includes the outbound number, type and quantity of the goods. Then, the current storage location of these goods in the warehouse is determined, and their path to the designated loading area in the warehouse is planned. Consider the layout of the warehouse and the storage location of the goods, as well as the availability and proximity of the loading area to determine the best outbound target location. For example, if the warehouse has multiple loading areas, select a loading area closest to the storage location of the goods to reduce handling time and cost. Consider the operation plan of the loading area to ensure that the selected loading area is free and ready for loading operations when the goods arrive. After determining the outbound target location of the goods, a goods outbound target location data will be generated. This data set records the outbound number, current storage location and target loading location of each piece of goods in detail. For example, it is identified that the goods with the outbound ID "100001" need to be moved from the 5th shelf of area B in the warehouse to "loading area 3".

[0124] Step S45: determining the outbound order of the stored goods based on the warehouse dynamic storage model according to the outbound goods list and the goods outbound target location data, and obtaining a goods outbound order list;

[0125] Specifically, the warehouse dynamic storage model can be used to determine the order of goods out of the warehouse, combined with the outbound goods list and the goods outbound target location data. The location, size, weight and outbound target location of each piece of goods, as well as the current traffic conditions in the warehouse and the status of the handling equipment, are analyzed to optimize the outbound order. Prioritize those goods that are urgently needed to be out of the warehouse or easy to carry to reduce the outbound time and improve efficiency. After the analysis is completed, a goods outbound order list will be generated, which clarifies the execution order of the outbound operations.

[0126] Step S46: Based on the warehouse dynamic storage model, the outbound goods list, the outbound goods target location data and the outbound goods sequence list are used to plan the outbound path of the stored goods, and the outbound goods path data are obtained. The outbound goods sequence list and the outbound goods path data are recorded as the outbound goods execution plan.

[0127] Specifically, the outbound path for each piece of cargo can be planned based on the warehouse dynamic storage model, combined with the outbound cargo list, cargo outbound target location data, and cargo outbound sequence list. The optimal outbound path is determined by analyzing the aisles, shelf layout, and working range of the handling equipment in the warehouse. The order of cargo handling can also be considered to avoid path intersections and conflicts. For example, a path is planned to first move cargo from area A of the warehouse, then area B, and finally area C to reduce the moving distance and time of the handling equipment. After the path planning is completed, a cargo outbound path dataset will be generated, which records the outbound path and sequence of each piece of cargo in detail.

[0128] Step S47: Automated outbound operations are performed on the stored goods according to the outbound execution plan, and the storage status of the second warehouse storage status matrix is ​​synchronously updated to obtain a third warehouse storage status matrix.

[0129] Specifically, the automated outbound operation can be performed according to the outbound execution plan. Automated handling equipment, such as automatic guided vehicles (AGVs), robots or conveyor systems, are directed to move the goods from the storage location to the outbound area according to the planned path and sequence. During the entire outbound process, the status of the handling equipment and the location of the goods are monitored in real time to ensure the accuracy and safety of the outbound operation. Once the goods are successfully outbound, the system will update the second warehouse storage status matrix and generate the third warehouse storage status matrix, which reflects the latest warehouse storage status after outbound.

[0130] The present invention realizes real-time updating of warehouse storage status through intelligent outbound management, ensuring the accuracy and reliability of storage management. By timely collecting outbound demand and generating a list of goods, the market can be quickly responded to, thereby improving customer satisfaction. By utilizing the warehouse three-dimensional model and dynamic storage status mapping, space utilization is optimized and space waste is reduced. By intelligently planning the outbound order and path, the efficiency of outbound operations is significantly improved, while the error rate is reduced and the accuracy of operations is ensured. Automated outbound operations reduce dependence on manual labor, reduce labor costs, and improve the stability and reliability of operations. In addition, by being able to update the storage status in real time after completing the outbound operation, accurate data support is provided for inventory management and decision-making, thereby improving overall logistics efficiency, shortening the delivery time of goods, accelerating the turnover of goods, and providing customers with faster and more reliable logistics services.

[0131] Preferably, step S5 comprises the following steps:

[0132] Step S51: extracting the storage duration of the stored goods according to the third warehouse storage state matrix to obtain a goods storage duration data set;

[0133] Specifically, the storage time of stored goods can be extracted using the third warehouse storage status matrix. The third warehouse storage status matrix is ​​a dynamically updated database that records the current storage location and storage time of all goods in the warehouse. Query this matrix, collect the warehousing time of each piece of goods, and calculate their storage time in the warehouse. Organize these data into a goods storage time dataset, which contains the ID, storage location, warehousing time, and current storage time of each piece of goods. For example, it is found that the goods with the ID "100001" have been stored for 30 days since they were put into storage.

[0134] Step S52: screening the cargo storage time data set according to the preset detention time threshold, and performing detention time statistics to obtain a detention cargo ID set and a detention time statistics table;

[0135] Specifically, the cargo storage time dataset can be screened according to the preset detention time threshold to identify the stranded cargo. Stranded cargo refers to cargo that has been stored in the warehouse for longer than the preset threshold. Set a detention time threshold, such as 30 days, and then filter out all cargo that has been stored for longer than this threshold from the cargo storage time dataset. Record the IDs of these cargoes, calculate their detention time, and generate a stranded cargo ID set and a detention time statistics table. For example, it is found that the cargo with the cargo ID "100002" has been stranded for 45 days.

[0136] Step S53: performing location statistics of the stranded goods according to the third warehouse storage status matrix, the stranded goods ID set and the stranded duration statistics table to obtain a goods stranded status report;

[0137] Specifically, the location statistics of the stranded goods can be combined with the third warehouse storage status matrix, the stranded goods ID set and the stranded time statistics table to generate a cargo stranded status report. Query the third warehouse storage status matrix to find the current storage location of all goods in the stranded goods ID set. Then, count the distribution of these goods in the warehouse, including their storage intervals, shelves and cargo locations. You can also analyze the storage duration and location information of the stranded goods to identify the goods with the longest stranded time and occupying key storage locations. This information will be integrated into the cargo stranded status report. For example, it was found that a batch of goods that had been stranded for 60 days were concentrated in Area A of the warehouse.

[0138] Step S54: Determine the order of leaving the warehouse for the detained goods according to the goods detention status report to obtain a departure order table for the detained goods;

[0139] Specifically, the order of outbound delivery of stranded goods can be determined based on the cargo detention status report. First, the data in the report will be analyzed, taking into account the storage duration, storage location and cargo characteristics, such as fragility or shelf life. The detention status report shows that some goods need to be handled first due to their long storage time, while other goods also need to be shipped out as soon as possible due to their characteristics (such as perishable food). Based on these factors, a stranded goods outbound order table is formulated, which clarifies the priority and order of outbound operations. For example, it is decided to first deal with perishable goods stored in area B, which have been detained for 60 days, and then heavy machinery parts stored in area A, which have been detained for a shorter time.

[0140] Step S55: Determine a scheduling plan for the stranded goods according to the stranded goods outbound sequence table, obtain an intelligent scheduling plan for the stranded goods, and perform outbound processing operations on the stranded goods in batches according to the intelligent scheduling plan for the stranded goods.

[0141] Specifically, a scheduling plan for stranded goods can be formulated based on the order table for the outbound delivery of stranded goods. The outbound delivery operation of stranded goods is planned by considering the current workload of the warehouse, the available handling equipment and personnel configuration, and the outbound destination of the goods. Advanced scheduling algorithms, such as genetic algorithms or simulated annealing algorithms, can be used to optimize the scheduling plan to ensure the efficiency of the operation and the rational use of resources. After the scheduling plan is formulated, an intelligent scheduling plan for stranded goods will be generated, which records in detail the expected outbound time, assigned handling equipment and personnel, and outbound path for each batch of stranded goods. Then, the batch outbound processing of stranded goods is performed according to this scheduling plan. For example, in the next work shift, AGV is first used to transport perishable goods stored in area B to the outbound delivery area, and then heavy machinery parts stored in area A are processed in the same shift.

[0142] The present invention significantly improves warehouse storage efficiency through intelligent management of stranded goods. By monitoring and analyzing the storage time of goods, it can quickly identify and process overdue stranded goods, thereby reducing unnecessary occupation of storage space and improving space utilization. At the same time, by intelligently planning the outbound order, the goods circulation speed is optimized, the outbound processing of stranded goods is accelerated, the risk of inventory backlog is effectively reduced, and the related losses and costs are reduced. By quickly responding to the outbound demand of stranded goods, the customer service level is significantly improved, and customer satisfaction and trust are enhanced. The present invention also shows a high degree of flexibility, and can flexibly adjust the outbound order and scheduling plan according to actual conditions, thereby improving the adaptability of warehouse management. The implementation of the intelligent scheduling plan for stranded goods ensures the accuracy and feasibility of the operation plan, reduces the uncertainty and potential errors in the operation process, thereby reducing logistics costs and improving the overall cost-effectiveness. The automated identification, statistics and scheduling process of stranded goods reduces manual operations and improves the automation level of warehouse operations.

[0143] Preferably, the present invention further provides an intelligent stereoscopic warehouse, comprising an intelligent control system, wherein the intelligent control system is used to execute the intelligent stereoscopic warehouse operation method as described above, and the intelligent stereoscopic warehouse comprises:

[0144] The cargo classification module is used to collect logistics tracking data of incoming cargo to obtain the current logistics tracking data set; classify incoming cargo according to the current logistics tracking data set to obtain a cargo classification list, wherein the cargo classification list includes a list of fragile cargo at several sites and a list of non-fragile cargo at several sites;

[0145] An allocation module is used to allocate storage intervals for incoming goods according to the goods classification list to obtain a goods storage interval allocation list; allocate storage locations for incoming goods according to the goods storage interval allocation list to obtain a goods storage location mapping table;

[0146] The cargo storage module is used to perform logistics updates on the current logistics tracking data set according to the cargo storage location mapping table, obtain an updated logistics tracking data set, and synchronize the updated logistics tracking data set to the preset logistics monitoring platform in real time; determine the handling plan for the incoming cargo according to the cargo storage location mapping table, and obtain the cargo incoming handling plan; perform cargo directional transportation operations on the incoming cargo according to the cargo storage location mapping table and the cargo incoming handling plan;

[0147] The outbound execution module is used to collect outbound demand of the intelligent stereoscopic warehouse and obtain an outbound goods list; determine the outbound plan for the stored goods according to the goods storage location mapping table and the outbound goods list to obtain an outbound execution plan; perform automated outbound operations on the stored goods according to the outbound execution plan to obtain a storage state matrix of the third warehouse;

[0148] The detention scheduling module is used to analyze the cargo detention of the intelligent stereoscopic warehouse according to the storage status matrix of the third warehouse to obtain a cargo detention status report; determine the scheduling plan for the intelligent stereoscopic warehouse according to the cargo detention status report to obtain an intelligent scheduling plan for the stranded cargo, and perform batch outbound processing of the stranded cargo according to the intelligent scheduling plan for the stranded cargo.

[0149] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0150] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent three-dimensional warehouse operation method, characterized in that: The following steps are involved: Step S1: Collect logistics tracking data of the incoming goods to obtain a current logistics tracking data set; classify the incoming goods according to the current logistics tracking data set to obtain a goods classification list, wherein the goods classification list includes a list of fragile goods at several sites and a list of non-fragile goods at several sites; Step S2: allocating storage intervals for incoming goods according to the goods classification list to obtain a goods storage interval allocation list; allocating storage locations for incoming goods according to the goods storage interval allocation list to obtain a goods storage location mapping table; Step S3: perform logistics update on the current logistics tracking data set according to the cargo storage location mapping table to obtain an updated logistics tracking data set, and synchronize the updated logistics tracking data set to the preset logistics monitoring platform in real time; determine the handling plan for the incoming cargo according to the cargo storage location mapping table to obtain the cargo incoming handling plan; perform cargo directional transportation operations on the incoming cargo according to the cargo storage location mapping table and the cargo incoming handling plan; Step S4: collecting outbound demand for the intelligent stereoscopic warehouse to obtain an outbound cargo list; determining an outbound cargo plan for the stored cargo according to the cargo storage location mapping table and the outbound cargo list to obtain an outbound cargo execution plan; performing automated outbound operations on the stored cargo according to the outbound cargo execution plan to obtain a storage state matrix of the third warehouse; Step S5: Cargo detention analysis is performed on the intelligent stereoscopic warehouse according to the storage status matrix of the third warehouse to obtain a cargo detention status report; a scheduling plan is determined for the intelligent stereoscopic warehouse according to the cargo detention status report to obtain an intelligent scheduling plan for the stranded goods, and the stranded goods are processed for outbound delivery in batches according to the intelligent scheduling plan for the stranded goods.

2. The intelligent high-bay warehouse operation method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Collect logistics tracking data for the incoming goods to obtain a current logistics tracking data set; Step S12: parsing the cargo information of the current logistics tracking data set to obtain a cargo attribute data set, wherein the cargo attribute data set includes a plurality of cargo attribute data, each cargo attribute data at least including cargo ID data, cargo size data, cargo weight data, cargo next station data and cargo fragility data; Step S13: clustering the incoming goods into sites according to the next site data of the goods, and obtaining a list of goods at several sites; Step S14: Classify the fragility of the site cargo list according to the cargo fragility data to obtain a site fragile cargo list and a site non-fragile cargo list; Step S15: After traversing the cargo lists of several sites, execute step S14 to obtain the fragile cargo lists of several sites and the non-fragile cargo lists of several sites, and record the fragile cargo lists of several sites and the non-fragile cargo lists of several sites as cargo classification lists.

3. The intelligent high-bay warehouse operation method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing a three-dimensional spatial scan on the intelligent stereoscopic warehouse to obtain a three-dimensional model of the warehouse, and performing cargo location recognition on the three-dimensional model of the warehouse to obtain an initial cargo location layout data set; Step S22: dividing the initial cargo location layout data set into storage intervals to obtain a storage interval division data set, wherein the storage interval division data set includes a plurality of storage intervals; Step S23: Based on the storage interval division data set, the space capacity and available cargo space statistics of the intelligent stereoscopic warehouse are performed to obtain a first warehouse storage state matrix, wherein the first warehouse storage state matrix includes the space capacity data and available cargo space data of each storage interval; Step S24: collecting distribution plan data for the cargo classification list based on the current logistics tracking data set to obtain a site distribution plan data set; Step S25: according to the site distribution plan data set, the outbound time of the goods to be stored corresponding to the site fragile goods list and the site non-fragile goods list is estimated to obtain the outbound time prediction data of the goods at the same site, wherein the site fragile goods list and the site non-fragile goods list belong to the same goods list of the next site; Step S26: After traversing the fragile goods lists of several stations and the non-fragile goods lists of several stations in the cargo classification list, execute step S25 to obtain the forecast data of the outbound time of several cargoes at the same station; Step S27: Allocate storage intervals for the incoming goods according to the first warehouse storage status matrix, the cargo classification list and the cargo outbound time prediction data of the same site to obtain a cargo storage interval allocation list, and allocate storage locations for the incoming goods according to the cargo storage interval allocation list to obtain a cargo storage location mapping table.

4. The intelligent high-bay warehouse operation method according to claim 3, characterized in that: Step S27 includes the following steps: Step S271: Based on the warehouse three-dimensional model, the data set is divided according to the storage intervals to perform structural stability evaluation on each storage interval, and obtain an interval structural stability data set; Step S272: performing vibration sensitivity analysis on each storage interval according to the storage interval division data set based on the warehouse three-dimensional model to obtain an interval vibration sensitivity data set; Step S273: collecting the current location data of the goods to be stored, and obtaining the current location data of the goods; Step S274: performing access path statistics of the incoming goods and the storage intervals based on the warehouse three-dimensional model and the current location data of the goods, and obtaining a goods access path data set; Step S275: Based on the storage interval division data set, the storage interval is demarcated as a seismic priority zone according to the interval structural stability data set and the interval vibration sensitivity data set to obtain seismic priority zone data, and the storage interval division data set is removed from the seismic priority zone according to the seismic priority zone data to obtain non-fragile goods priority zone data; Step S276: obtaining a cargo attribute data set, performing storage interval demand statistics on fragile cargo according to the cargo attribute data set and the fragile cargo list of the site, obtaining fragile cargo storage demand data, and allocating storage intervals for fragile cargo according to the fragile cargo storage demand data and the earthquake-proof priority zone data, obtaining a fragile cargo storage interval allocation table; Step S277: performing storage interval demand statistics on non-fragile goods according to the goods attribute data set and the non-fragile goods list of the site to obtain non-fragile goods storage demand data, and allocating storage intervals for non-fragile goods according to the goods access path data set and the non-fragile goods storage demand data based on the non-fragile goods priority area data to obtain a non-fragile goods storage interval allocation table, and recording the fragile goods storage interval allocation table and the non-fragile goods storage interval allocation table as a goods storage interval allocation list; Step S278: Allocate storage locations for incoming goods according to the goods storage interval allocation list, the goods classification list and the goods attribute data set to obtain a goods storage location mapping table.

5. The intelligent high-bay warehouse operation method according to claim 4, characterized in that: Step S278 includes the following steps: Step S2781: Based on the warehouse three-dimensional model, the storage locations in the cargo storage area allocation list are identified in height, and a storage area location height mapping table is obtained; Step S2782: sorting the fragile goods list and the non-fragile goods list of the site in descending order according to the goods weight according to the goods attribute data set, to obtain a fragile goods weight sorting table and a non-fragile goods weight sorting table; Step S2783: According to the storage interval cargo height mapping table, the cargo storage interval allocation list, the fragile cargo weight sorting table and the non-fragile cargo weight sorting table, high priority storage allocation is performed on the corresponding cargo to be stored, and a cargo storage location mapping table is obtained.

6. The intelligent high-bay warehouse operation method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: real-time positioning of the incoming goods is performed to obtain a real-time goods positioning data set, and the real-time goods positioning data set is supplemented with a position according to a goods storage location mapping table to obtain a detailed real-time goods positioning data set; Step S32: updating the current logistics tracking data set according to the detailed real-time cargo location data set to obtain an updated logistics tracking data set, and synchronizing the updated logistics tracking data set to a preset logistics monitoring platform in real time; Step S33: collecting transport resource parameters of the intelligent stereoscopic warehouse to obtain a transport vehicle resource data set, wherein the transport vehicle resource data set includes a plurality of transport vehicle resource data, each transport vehicle resource data at least includes a maximum load capacity, a carriage size data, energy reserve data, a drivable path data, a vehicle working status data and a vehicle identification code; Step S34: screening the transport vehicle resource data set for availability according to the energy reserve data and the vehicle working status data to obtain an available transport vehicle data set; Step S35: Determine a transportation plan for the incoming goods according to the available transport vehicle data set and the cargo storage location mapping table to obtain a cargo incoming transportation plan; Step S36: Carry out directional transportation of the cargo to be stored in accordance with the cargo storage location mapping table and the cargo storage and handling plan.

7. The intelligent high-bay warehouse operation method according to claim 6, characterized in that: Step S35 includes the following steps: Step S351: Evaluate the energy status of available transport vehicles according to the energy reserve data to obtain an energy status chart of the transport vehicles; Step S352: Classifying the available transport vehicles into transport types according to the maximum load mass, the vehicle compartment size data and the transport vehicle energy status chart, and obtaining a transport vehicle transport type classification table, wherein the transport vehicle transport type classification table includes a long-distance transport type and a short-distance transport type; Step S353: Obtain the warehouse three-dimensional model and the current location data of the goods, and classify the goods to be stored in the warehouse by transportation type based on the warehouse three-dimensional model according to the goods storage location mapping table and the current location data of the goods, and obtain a cargo transportation type classification table, wherein the cargo transportation type classification table includes long-distance transportation type and short-distance transportation type; Step S354: matching the incoming goods with transport vehicles according to the cargo handling type classification table and the transport vehicle handling type classification table to obtain a transport vehicle matching result table; Step S355: Obtain a cargo attribute data set, and plan the transport batches of the cargo to be stored according to the cargo attribute data set, the maximum load mass, the compartment size data, the transport vehicle matching result table and the cargo storage location mapping table to obtain a transport batch planning table; Step S356: Obtain a cargo access path data set, and determine the transportation path for the cargo to be stored based on the cargo access path data set and the cargo storage location mapping table, obtain a transportation path data table, and record the transport batch planning table and the transportation path data table as a cargo storage and transportation plan.

8. The intelligent high-bay warehouse operation method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: obtaining a first warehouse storage state matrix, and synchronously updating the storage state of the first warehouse storage state matrix according to the cargo storage location mapping table to obtain a second warehouse storage state matrix; Step S42: collecting outbound demand from the intelligent stereoscopic warehouse to obtain an outbound goods list; Step S43: obtaining a three-dimensional warehouse model, and performing warehouse storage state mapping on the three-dimensional warehouse model according to the second warehouse storage state matrix to obtain a dynamic storage model of the warehouse; Step S44: extracting the outbound target location from the outbound goods list to obtain the outbound target location data of the goods; Step S45: determining the outbound order of the stored goods based on the warehouse dynamic storage model according to the outbound goods list and the goods outbound target location data, and obtaining a goods outbound order list; Step S46: Based on the warehouse dynamic storage model, the outbound goods list, the outbound goods target location data and the outbound goods sequence list are used to plan the outbound path of the stored goods, and the outbound goods path data are obtained. The outbound goods sequence list and the outbound goods path data are recorded as the outbound goods execution plan. Step S47: Automated outbound operations are performed on the stored goods according to the outbound execution plan, and the storage status of the second warehouse storage status matrix is ​​synchronously updated to obtain a third warehouse storage status matrix.

9. The intelligent high-bay warehouse operation method according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: extracting the storage duration of the stored goods according to the third warehouse storage state matrix to obtain a goods storage duration data set; Step S52: screening the cargo storage time data set according to the preset detention time threshold, and performing detention time statistics to obtain a detention cargo ID set and a detention time statistics table; Step S53: performing location statistics of the stranded goods according to the third warehouse storage status matrix, the stranded goods ID set and the stranded duration statistics table to obtain a goods stranded status report; Step S54: Determine the order of leaving the warehouse for the detained goods according to the goods detention status report to obtain a departure order table for the detained goods; Step S55: Determine a scheduling plan for the stranded goods according to the stranded goods outbound sequence table, obtain an intelligent scheduling plan for the stranded goods, and perform outbound processing operations on the stranded goods in batches according to the intelligent scheduling plan for the stranded goods.

10. An intelligent three-dimensional warehouse, characterized in that: The intelligent control system is used to execute the intelligent high-bay warehouse operation method according to claim 1. The intelligent high-bay warehouse comprises: The cargo classification module is used to collect logistics tracking data of incoming cargo to obtain the current logistics tracking data set; classify incoming cargo according to the current logistics tracking data set to obtain a cargo classification list, wherein the cargo classification list includes a list of fragile cargo at several sites and a list of non-fragile cargo at several sites; An allocation module is used to allocate storage intervals for incoming goods according to the goods classification list to obtain a goods storage interval allocation list; allocate storage locations for incoming goods according to the goods storage interval allocation list to obtain a goods storage location mapping table; The cargo storage module is used to perform logistics updates on the current logistics tracking data set according to the cargo storage location mapping table, obtain an updated logistics tracking data set, and synchronize the updated logistics tracking data set to the preset logistics monitoring platform in real time; determine the handling plan for the incoming cargo according to the cargo storage location mapping table, and obtain the cargo incoming handling plan; perform cargo directional transportation operations on the incoming cargo according to the cargo storage location mapping table and the cargo incoming handling plan; The outbound execution module is used to collect outbound demand of the intelligent stereoscopic warehouse and obtain an outbound goods list; determine the outbound plan for the stored goods according to the goods storage location mapping table and the outbound goods list to obtain an outbound execution plan; perform automated outbound operations on the stored goods according to the outbound execution plan to obtain a storage state matrix of the third warehouse; The detention scheduling module is used to analyze the cargo detention of the intelligent stereoscopic warehouse according to the storage status matrix of the third warehouse to obtain a cargo detention status report; determine the scheduling plan for the intelligent stereoscopic warehouse according to the cargo detention status report to obtain an intelligent scheduling plan for the stranded cargo, and perform batch outbound processing of the stranded cargo according to the intelligent scheduling plan for the stranded cargo.

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