Warehousing multi-factor feedback mechanism collaborative sorting method and system

Abstract

The invention relates to the field of intelligent warehouse management, and discloses a warehouse multi-factor feedback mechanism collaborative sorting method and system, and the method comprises the following steps: generating timestamp data based on warehousing time, and carrying out the classified storage according to timestamps and money box attributes; analyzing an order demand, disassembling the order demand into a whole box and a scattered box, and screening an available inventory range through a timestamp; sorting is executed in a layered mode according to the first-in first-out principle, the whole pallet inventory with the earliest timestamp is called preferentially, when the whole pallet inventory is insufficient, a greedy algorithm is adopted to optimize scattered pallet combination, and inherited timestamp splitting operation is conducted on mixed pallets; after support disassembly, support closing is carried out according to type differentiation, the original sealing coupons are independently closed, original timestamps are bound, and a timestamp mapping relation table is generated during cross-batch support closing of non-original sealing coupons; and the tray disassembling, tray assembling and warehouse-out processes are controlled through the task state machine, the matching performance of the timestamp and the inventory state is verified in real time, and the operation log is dynamically updated. According to the invention, the batch accurate tracing of the allocation operation is realized, the resource utilization rate is improved, and the controllability of the operation link is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent warehouse management technology, and in particular to a warehouse multi-factor feedback mechanism collaborative picking method and system. Background Art

[0002] In warehousing and allocation operations, the accuracy, efficiency, and traceability of inventory management and picking operations are core challenges. Existing technologies typically rely on static inventory classification and fixed picking rules, making it difficult to dynamically adapt to complex inventory structures with multiple batches and types. This is particularly true when handling mixed pallet boxes. Due to the lack of a mechanism to inherit and associate original batch data, the disassembled boxes cannot be effectively traced back to the original incoming batch, resulting in a disconnect between inventory status and physical operations.

[0003] Furthermore, existing picking strategies often match based on a single dimension (such as quantity or amount), failing to incorporate dynamic factors like timestamps to optimize picking order. This can easily lead to deviations in the execution of first-in-first-out rules and waste inventory resources. During the task status flow, asynchrony between inventory data updates and operation execution is common, such as delayed inventory marking after pallet removal or loss of association between palletized batches, further exacerbating the risk of inventory discrepancies.

[0004] How to achieve precise batch control, dynamic picking decisions and real-time status synchronization throughout the entire inventory life cycle has become a technical challenge that needs to be solved urgently. Summary of the Invention

[0005] The purpose of the present invention is to provide a warehouse multi-factor feedback mechanism collaborative picking method and system, which solves the problems of low picking efficiency, batch mixing and inconsistency between accounts and actuals caused by static inventory classification, broken batch traceability links and asynchronous operation status in existing warehouse allocation operations.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention provides a warehouse multi-factor feedback mechanism collaborative picking method, comprising the following steps: S1. Generate timestamp data for the incoming payment box and verify the integrity and uniqueness of the timestamp; the timestamp data is generated based on the incoming payment date; S2. Based on the timestamp data, the cash boxes are classified and stored in a designated area according to material name, ticket type, type, and mark, and cash boxes with the same timestamp are sorted according to the first-in-first-out rule; S3. Analyze transfer order requirements, break them down into full-case and bulk-case requirements, and filter inventory ranges based on timestamp data. S4. Verify that inventory meets demand. If inventory is insufficient, provide feedback on inventory shortfall information with specific coupon type and timestamp. S5. Call inventory resources in timestamp order and perform picking operations in sequence: whole pallet priority, loose pallet replenishment, mixed pallet splitting, and update picking results based on timestamp data. S6. The unpacked cash boxes are collocated. The collocation rules perform differentiated operations based on the timestamp data and cash box type. S7. The process of unpalletizing, combining and shipping out is controlled through the task status transfer mechanism. The status transfer depends on the update and verification of the timestamp data.

[0007] Preferably, in the step S1, the generation of the timestamp is linked to the warehousing device through an interface, written into the database in real time, and the format validity of the timestamp is verified.

[0008] Preferably, in step S2, the classified storage includes: The sealed coupons are stored separately according to the timestamp and coupon type; Non-sealed coupons are stored mixedly according to timestamps, and the storage location is bound to the timestamp data.

[0009] Preferably, in step S4, the feedback of inventory shortage information of specific coupon type and timestamp includes: The type of insufficient stock, timestamp, and quantity of the shortfall; Recommended replenishment timestamp range.

[0010] Preferably, in the step S5, when whole pallets are picked first, the whole pallet box with the earliest timestamp is randomly selected, and the picking timestamp range is recorded.

[0011] Preferably, in step S5, the mixed and supported splitting operation includes: When splitting mixed cash boxes, retain the original timestamp data; The split cash boxes are stored back in the box library and marked as the inventory corresponding to the original timestamp.

[0012] Preferably, in the step S5, when the loose pallet is supplemented and picked, a greedy algorithm is used to combine the number of loose pallet boxes, and the input parameters of the greedy algorithm include the timestamp priority and the combination of the number of loose pallet boxes.

[0013] Preferably, in step S6, the joint support rule is: The sealed coupons are recorded in separate consignment records according to the timestamp and coupon type; When non-sealed bills are combined and consigned together, a mapping table between timestamps and consignment results is generated.

[0014] Preferably, in step S7, the task status transfer includes: After the removal task is completed, the timestamp data is updated; After the consignment task is completed, the transfer order is bound and marked as picked.

[0015] The present invention also provides a warehouse multi-factor feedback mechanism collaborative picking system for executing the method, comprising: Timestamp generation module, used to generate and verify timestamp data for incoming payment boxes; The classification storage module classifies cash boxes by material name, voucher type, type, and mark based on timestamp data, and stores them in order by timestamp; The dynamic picking module calls the full pallet, loose pallet, and mixed pallet inventory in timestamp order and uses a greedy algorithm to optimize the picking path; The splitting and combining module retains the original timestamp data when splitting mixed pallet inventory and performs classified combining operations; The task status management module relies on timestamp data to update the status of depalletizing, consolidating and outbound tasks.

[0016] In summary, the present invention includes at least one of the following beneficial technical effects: 1. This invention uses timestamp-driven multi-dimensional classification storage and first-in-first-out rules to force the priority call of inventory resources with the earliest timestamp. Combined with dynamic picking strategies, it significantly reduces invalid inventory traversal operations, avoids picking errors caused by batch mixing, and directly reduces the probability of cross-batch misoperation, thereby shortening the transfer order processing cycle.

[0017] 2. This invention is based on the timestamp inheritance mechanism and the generation of a combined storage mapping relationship table. The independent boxes after the mixed storage split inherit the original timestamp. The mixed storage operation records the original batch association through the mapping table. Even after the physical merger, it can still be traced back to the initial inventory batch through the data link, providing a complete batch traceability basis for subsequent audits.

[0018] 3. The present invention uses the task state machine and atomic update of timestamp data to verify the matching of timestamps and inventory records in real time during the operations of unpalletizing, combining, and shipping, and synchronously updates the inventory status mark to avoid abnormal inventory status due to data asynchrony.

[0019] 4. The present invention optimizes the combination of loose pallets driven by differentiated palletizing rules and greedy algorithms, allowing non-sealed bills to be combined across timestamps. At the same time, it dynamically matches the number of loose pallet boxes with the goal of minimizing the number of pallet removals, reducing the impact of pallet removal operations on the inventory structure, thereby improving the space utilization of the pallet warehouse and the box warehouse.

[0020] 5. This invention relies on a dynamic picking range screening and multi-level verification mechanism based on timestamps. The system only calls inventory within the order requirement timestamp range, and key operations must pass timestamp consistency verification, avoiding the risk of misuse of expired inventory from the data source and ensuring the compliance of allocation operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the method flow of the present invention; Figure 2 Schematic diagram of the system structure of the present invention.

[0022] Among them, 10, timestamp generation module; 20, classification storage module; 30, dynamic picking module; 40, unpacking and combining module; 50, task status management module. DETAILED DESCRIPTION

[0023] The following is combined with Figure 1 -Attached Figure 2 , the present invention is described in further detail.

[0024] The present invention provides a collaborative picking method with a warehousing multi-factor feedback mechanism, which drives inventory classification, picking logic and task status flow through timestamp data to achieve efficient and accurate transfer order processing.

[0025] like Figure 1 As shown, the warehouse multi-factor feedback mechanism collaborative picking method may include the following steps: S1. Generate timestamp data for the incoming payment box and verify the integrity and uniqueness of the timestamp; S2. Based on the timestamp data, the cash boxes are classified and stored in the designated area according to the material name, ticket type, type, and mark, and the cash boxes with the same timestamp are sorted according to the first-in-first-out rule; S3. Analyze transfer order requirements, break them down into full-case and bulk-case requirements, and filter inventory ranges based on timestamp data. S4. Verify that inventory meets demand. If inventory is insufficient, provide feedback on inventory shortfall information with specific coupon type and timestamp. S5. Call inventory resources in timestamp order and perform picking operations in sequence: whole pallet priority, loose pallet replenishment, mixed pallet splitting, and update picking results based on timestamp data. S6. The unpacked cash boxes are collocated. The collocation rules perform differentiated operations based on the timestamp data and cash box type. S7. The process of unpalletizing, combining and shipping out is controlled through the task status transfer mechanism. The status transfer depends on the update and verification of timestamp data.

[0026] As shown in Table 1, incoming cash boxes are categorized and stored in different areas based on timestamp data. Sealed bills are stored separately by bill type, while unsealed bills are mixed and stored by timestamp, but with associated storage coordinates. During picking, palletized, loose, and mixed inventory is retrieved in timestamp order. After unpalletizing, cash boxes retain their original timestamps and are stored in the box library. Different types of containers (e.g., A1 boxes, A2 boxes, and cartons) are distinguished by material name, bill type, and type. During palletizing operations, sealed bills are stored separately, while unsealed bills are mixed and stored, with timestamp mappings recorded.

[0027] Table 1 Classification of different types of containers

[0028] Note: Full pallet: 20 or more cartons with the same material name, voucher type, type, and mark.

[0029] Loose pallet: less than 20 boxes with the same material name, ticket type, type and mark.

[0030] Mixed pallet: different material names, vouchers, types, and markings.

[0031] Full box: Banknotes of the same material name, denomination, and type, and filled with 20,000 pieces. Coins of the specified number of each denomination.

[0032] Loose Box: Banknotes of the same material name, denomination, and type, with less than 20,000 pieces. Coins with insufficient quantities of each denomination.

[0033] When consolidating shipments, if the type is sealed, consolidation is done by material name and ticket type. If the types are unchecked damaged notes, unsorted complete notes, or cleared and sorted notes, they can all be put on one pallet.

[0034] In step S1, timestamp data is generated for the incoming cash box and verified for integrity and uniqueness. This timestamp data is generated based on the actual incoming date of the cash box and is used to uniquely identify the incoming batch to which the cash box belongs, ensuring traceability across batches. Timestamp generation is achieved through a system interface linked to devices in the incoming process. Specifically, once a cash box completes information collection via incoming equipment (such as a barcode scanner or radio frequency identification device), the system automatically extracts the current incoming date, generates timestamp data in a pre-set format, and writes this data to the database's inventory management module in real time. The generated timestamp data is formatted as a continuous numerical sequence consisting of the year, month, and day, such as "20230507," representing batch time sequences in a standardized manner.

[0035] Furthermore, the system performs an integrity check on the timestamp data, specifically including: checking whether the timestamp field is empty, whether the number of digits of the timestamp complies with the preset rules, and whether the date value of the timestamp is within the valid range (for example, future dates or illegal date values ​​are not allowed). If the check fails, the system generates an alarm log and suspends the current warehousing process. Manual intervention is required to correct the erroneous data and re-execute the check. At the same time, the system verifies the uniqueness of the timestamp to ensure that the timestamp data of the boxes in the same batch are completely consistent, and the timestamps of different batches are strictly incremented in the order of the warehousing date to avoid batch confusion. Preferably, the generation of timestamps can be achieved through compatible interfaces with a variety of warehousing equipment, such as barcode scanners, RFID readers or automated transmission line terminal equipment, to meet the hardware adaptation requirements of different warehousing scenarios.

[0036] In practice, the timestamp generation logic is deeply integrated with the batch management module of the inventory management system. When a cash box enters the warehouse, the system triggers a timestamp generation event based on the entry operation. This event retrieves the current date by calling the system clock service and generates a timestamp string based on pre-set format conversion rules. The generated string is encrypted and written to the database's batch index table, where it is stored in association with the cash box's attributes, such as the material name, voucher type, and type. Preferably, for cash boxes with existing timestamps (e.g., in secondary entry scenarios involving cross-warehouse transfers), the system retains the original timestamp data and skips the generation step, ensuring batch identification consistency.

[0037] Through the above technical means, the timestamp data generation and verification mechanism provides basic data support for subsequent inventory classification, picking priority control and task status flow, while avoiding timestamp errors or duplications caused by human operations, ensuring the accurate implementation of the first-in-first-out principle of the inventory management process.

[0038] In step S2, cash boxes are categorized and stored in multiple dimensions based on timestamp data, and cash boxes with the same timestamp are sorted according to a first-in, first-out (FIFO) order. Dimensions of categorization include material name, voucher type, type, and tag. By combining cash box attributes with timestamp data, a hierarchical inventory management structure is formed. Specifically, the system divides storage areas based on the material name of the cash box, subdivides shelf locations within each area by voucher type, and further assigns specific storage location coordinates based on type and tag, achieving a precise mapping of physical space and data attributes.

[0039] Furthermore, differentiated storage strategies are implemented for sealed and non-sealed notes. Sealed notes are stored independently in designated shelf areas according to timestamps and note types. For example, sealed notes of 100 yuan with the same timestamp are stored in Area A1 of the pallet warehouse and separated from other note types by physical isolation devices. Non-sealed notes are mixed and stored in independent areas according to timestamps, but the storage location is bound to the timestamp data. For example, uncleared notes and damaged notes with a timestamp of 20230507 are stored together in Area B, and the system records the association between their storage location coordinates and timestamps. Preferably, the storage strategy can be implemented through label recognition technology, such as setting an electronic tag corresponding to the timestamp on a shelf or container to assist in the positioning and retrieval of the note box.

[0040] During the storage process, the system arranges the cash boxes with the same timestamp in the order of entry into the warehouse, and executes the first-in-first-out rule based on the temporal nature of the timestamp data. Specifically, when a new batch of cash boxes enters the warehouse, the system automatically places it at the end of the storage area with the same ticket type, and gives priority to the cash boxes stored at the front end during picking. To implement this rule, the system maintains a sorting index of timestamps and storage location coordinates in the inventory table of the database, and ensures that the physical storage order of the cash boxes is consistent with the data sorting by dynamically updating the index value. Preferably, the update of the sorting index can be achieved by real-time scanning of the storage location status. For example, when a cash box in a certain storage location is moved out, the system automatically adjusts the index value of the cash box in the subsequent storage location.

[0041] Through the aforementioned technical means, the categorized storage and sorting mechanism provides a structured inventory foundation for subsequent picking operations. Separate storage of sealed bills prevents mixing of boxes of different types, while timestamp-bound storage of unsealed bills improves space utilization without compromising sorting accuracy. Furthermore, the automated implementation of first-in, first-out rules ensures the controllable timing of inventory turnover, laying the foundation for the efficient processing of transfer orders.

[0042] In step S3, the transfer order demand is parsed and broken down into full-case and bulk-case requirements, while the available inventory range is filtered based on timestamp data. The parsing process extracts the material name, ticket type, and quantity information from the order through the order processing module. Combining inventory classification rules, the demand is broken down into full-case and bulk-case requirements. Specifically, full-case requirements are calculated by maximizing the number of full cases required to satisfy the order, while the remaining unfulfilled quantity is treated as bulk-case requirements. For example, if the total order demand is 100,000 pieces, it is broken down into five full cases (each containing 20,000 pieces) and no bulk-case requirements. Preferably, the splitting logic is implemented using a pre-defined integer division and remainder algorithm to ensure that full-case requirements are prioritized and maximized.

[0043] Furthermore, the system filters the inventory range involved in picking based on timestamp data. Specifically, only inventory with a timestamp earlier than or equal to the order requirement date is selected for picking to avoid using batches that are not yet available. For example, when the order requirement date is 20230507, only inventory with a timestamp of 20230507 and earlier is filtered. The filtering process is implemented through database query instructions. The system generates dynamic query conditions based on the ticket type, material name and timestamp range in the order, such as executing the "WHERE timestamp ≤ order_date AND material = 'xxx'" filtering operation in the inventory table.

[0044] During the decomposition and screening process, the system deeply binds order requirements to inventory classification rules. For example, a request for a full case of sealed coupons will only match palletized inventory with the same coupon type and timestamp, while a request for a loose case of unsealed coupons can be combined across timestamps. Preferably, this binding logic is implemented through multi-dimensional conditional judgments. For example, if the order type is sealed coupons, the system mandates that the full case request must be met by palletized inventory with the same timestamp; otherwise, an insufficient inventory alert will be triggered.

[0045] Through these technologies, order parsing and inventory screening mechanisms define clear inventory ranges and demand targets for subsequent picking operations. Prioritizing the decomposition of full-case requests reduces the frequency of loose-case operations, while timestamp-driven inventory screening ensures strict implementation of the first-in, first-out principle and avoids the risk of inventory misuse due to batch confusion.

[0046] In step S4, a demand matching check is performed on the filtered inventory range, and inventory gap information is fed back based on the verification results. This verification process includes verifying the matching degree between full-case demand and full-pallet inventory, verifying the matching amount between loose-case demand and loose-pallet inventory, and verifying the feasibility of splitting mixed-pallet inventory. Specifically, based on the full-case and loose-case demand split from step S3, the system traverses the inventory records within the corresponding timestamp range, calculates whether the quantity and amount of available full-pallet, loose-pallet, and mixed-pallet inventory meet the order requirements, and generates a verification result mark.

[0047] Furthermore, if the inventory is insufficient, the system generates feedback information containing the specific coupon type, timestamp, and number of gaps. For example, when the order demand is 5 pallets of 100-yuan coupons in a box, if the stock of the whole pallet with the timestamp of 20230507 is only 3 pallets, the feedback gap number is 2 pallets, and the specific timestamp identifier is associated. The feedback information is realized by dynamically generating a data packet in JSON or XML format, which contains the following fields: coupon code, timestamp, gap type (whole box / loose box), gap quantity / amount, and recommended replenishment timestamp range. Preferably, the recommended replenishment timestamp range is automatically generated by analyzing the historical inventory turnover rate and the timestamp distribution of adjacent batches. For example, the timestamp of the recommended next available batch is a subsequent date of the current timestamp.

[0048] In the full case demand verification, the system gives priority to matching the full pallet inventory with the same timestamp. If the full pallet quantity is insufficient, it is marked as a full case gap and an alarm is triggered. For loose case demand, the system traverses the loose pallet inventory with the same timestamp. If the loose pallet amount is insufficient, it further checks the feasibility of splitting the mixed pallet inventory. Preferably, the mixed pallet splitting feasibility verification includes the following logic: check whether the mixed pallet box contains independent packaging units of the current ticket type, and whether the remaining inventory after splitting still meets the mixed pallet integrity threshold. If the mixed pallet cannot be split or still cannot meet the demand after splitting, it is marked as a loose case gap.

[0049] Through these technical means, the inventory verification and feedback mechanism provides precise shortfall detection for order fulfillment. Layered verification of full and loose containers reduces ineffective inventory traversal, while the binding of shortfall information with timestamps facilitates subsequent replenishment planning. Furthermore, predicting the feasibility of mixed pallet splitting prevents ineffective splitting operations from impacting inventory structure, ensuring stable inventory management.

[0050] In step S5, inventory resources are called in timestamp order and picking operations are performed in layers, including whole pallet priority picking, loose pallet supplementary picking, and mixed pallet split picking. At the same time, the associated timestamp data is dynamically updated according to the picking results. The execution logic of the picking operation is based on the temporal priority of the timestamp, and the inventory resources with the earliest timestamp are called first to ensure the strict implementation of the first-in-first-out principle. Specifically, whole pallet priority picking traverses the whole pallet inventory with the earliest timestamp under the same ticket type, randomly selects available whole pallets for allocation, and records the timestamp range that has been picked, for example, marking the whole pallet box with the timestamp 20230507 as allocated.

[0051] Furthermore, when full pallet inventory is insufficient to meet demand, the system triggers the loose pallet replenishment picking process. Loose pallet replenishment uses a greedy algorithm to combinatorially optimize the number of loose pallet boxes at the same timestamp. The algorithm uses timestamp priority as the first weight and maximizing the number of loose pallet boxes as the second weight to generate the optimal combination of box numbers. For example, when the demand is 15 boxes, if the number of loose pallet boxes at the same timestamp is [10, 5, 3, 2], the combination of 10 boxes and 5 boxes is preferred to meet the demand. Preferably, the input parameters of the greedy algorithm include the time sequence value of the timestamp (e.g., 20230507 is earlier than 20230508) and the available set of loose pallet boxes, and the output is the minimum combination of box numbers that meets the demand.

[0052] For the picking of mixed pallet inventory, the system performs a mixed pallet splitting operation. During the splitting process, the original timestamp data of the mixed pallet cash box is inherited to the independent cash box after the splitting. For example, after the mixed pallet cash box with the timestamp of 20230507 is split into several independent boxes, each box is still marked as batch 20230507. The split cash box is stored back in the box library, and its storage location and status mark are updated in the inventory table to ensure that the association with the original timestamp is not lost. Preferably, the splitting operation is achieved by scanning the independent packaging unit identification of the mixed pallet cash box, for example, extracting the original batch information based on the QR code or RFID tag on the box.

[0053] During the picking process, the system updates the timestamp data of the picking results in real time. Specifically, after each full or loose pallet picking operation, the system marks the boxes within the corresponding timestamp range as allocated in the inventory record and generates a picking log, recording the operation time, operation type (full pallet / loose pallet / mixed pallet split), and associated timestamp information. Preferably, this timestamp update mechanism is implemented through database transactions to ensure the atomicity of picking operations and data updates, and avoid data inconsistencies caused by system interruptions.

[0054] Through these technical measures, the tiered picking mechanism achieves efficient inventory resource utilization while maintaining the first-in, first-out principle. Prioritizing whole pallets reduces the frequency of pallet removal operations, while algorithmic optimization improves the rationality of case combinations through loose pallet replenishment. Inherited timestamp management for mixed pallet splitting avoids batch confusion. Furthermore, dynamic updates of timestamp data provide an accurate data foundation for subsequent inventory status tracking and task flow.

[0055] In step S6, differentiated consolidation processing is performed on the unpacked cash boxes, and consolidation records and mapping relationships are generated based on the timestamp data and cash box type. Consolidation processing applies different rules to sealed and non-sealed bills: For sealed bills, the system generates independent consolidation records based on the timestamp and bill type, ensuring that a consolidation only includes cash boxes with the same timestamp, bill type, and type. For non-sealed bills (such as unchecked damaged bills or unsorted complete bills), mixed consolidation across timestamps or bill types is allowed, but a mapping table between timestamps and consolidation results is required to maintain batch traceability.

[0056] Specifically, the consolidation operation for sealed banknotes scans the timestamp and banknote type information of the unpalletized boxes, selects individual boxes that meet the requirements (same timestamp, same banknote type), and consolidates them into a new full pallet. During the consolidation process, the system creates a new consolidation record in the inventory management table, linking the original timestamp data and the coordinates of the post-consolidation location. For example, if a loose box of sealed 50-yuan banknotes with a timestamp of 20230507 is consolidated into a full pallet, a separate inventory entry is generated, tied to the original timestamp. Preferably, the consolidation record is updated atomically through database transactions to ensure consistency of total inventory before and after the consolidation.

[0057] For combined pallets of non-sealed bills, the system allows combining boxes with different timestamps or bill types into the same pallet. However, the mapping table must record the correspondence between the original timestamps and the combined results. For example, when combining a damaged bill with a timestamp of 20230507 and an outstanding bill with a timestamp of 20230508 into a single pallet, the mapping table records the pallet ID and the timestamp list it contains. The mapping table is stored using a key-value structure, for example, with the combined bill ID as the primary key and associated fields including a timestamp set, a bill type list, and the timestamp of the combined operation, providing the data foundation for subsequent inventory traceability.

[0058] During the consolidation process, the system dynamically updates the inventory status and associated data of the boxes. After the consolidation is completed, the individual boxes are removed from the box library, and the combined pallet is stored in the pallet library and marked as "consolidated" in the inventory table. At the same time, the system binds the consolidation record to the transfer order number and generates an operation log containing a timestamp, consolidation ID, and transfer order number, for example, recording metadata of the consolidation operation in JSON format. Preferably, the binding operation is implemented through a foreign key association in the database to ensure the queryability of the transfer order and consolidation record.

[0059] Through the aforementioned technical means, a differentiated consolidation mechanism meets inventory management standards while balancing operational efficiency and data traceability. Independent consolidation of sealed bills avoids the risk of batch mixing, while mixed consolidation of non-sealed bills improves space utilization. The introduction of a timestamp mapping table simplifies physical management while still enabling accurate inventory traceability through data association.

[0060] In step S7, the entire lifecycle of the unpalletizing, consolidating, and shipping processes is controlled through the task status flow mechanism, and the traceability of the operation chain is achieved by relying on the real-time update and verification of timestamp data. Based on the preset task state machine model, the state flow mechanism defines unpalletizing, consolidating, and shipping operations as independent task nodes. The status change of each node triggers the update and associated verification of the timestamp data. For example, after the unpalletizing task is completed, the system automatically triggers the status mark update of the timestamp data, changing the unpalletized cash box from the "in-stock" state to the "unpalletized and awaiting consolidation" state, and generates an operation log to record the details of the timestamp change.

[0061] Specifically, the status flow of the unpalletizing task is achieved by verifying the integrity of the timestamp of the unpalletized cash box. When the unpalletizing operation is executed, the system verifies whether the original timestamp of the unpalletized cash box is consistent with the inventory record. If the verification passes, the separated independent boxes inherit the original timestamp data and are stored in the box library. At the same time, the status of the corresponding timestamp in the inventory table is updated to "unpalletized". If the verification fails (for example, the timestamp does not match the inventory record), the system terminates the current task and triggers an alarm, requiring manual intervention to correct the data and re-execute the verification. Preferably, the verification process is implemented atomically through database transactions to ensure data consistency.

[0062] For the consignment task, the system will bind the consignment record with the transfer order number after the consignment operation is completed, and mark the task status as "consolidated and waiting to be shipped out". During the binding process, the system verifies the consistency of the timestamp data of the consignment box and the mapping relationship table, such as verifying whether the timestamp list in the consignment record of non-sealed bills completely covers the original timestamps of all the combined boxes. After the verification is passed, the entire consignment box is stored in the pallet warehouse and marked as "consolidated" in the inventory table. At the same time, an associated log containing the timestamp, consignment ID and transfer order number is generated. Preferably, the binding operation is implemented by calling the interface of the external transfer system to ensure real-time synchronization of the transfer order status and the inventory status.

[0063] The status flow of the outbound task is processed differently according to the outbound time. For orders that are shipped out on the same day, the cash boxes after consolidation are directly moved to the inspection area and marked as "waiting for shipment"; for orders that are not shipped out on the same day, the system will re-deposit the consolidation cash boxes into the pallet warehouse and mark them as "consolidated and waiting for shipment". When the outbound task is executed, the system will once again verify the consistency of the timestamp data of the cash box with the transfer order requirements, such as verifying whether the timestamp of the outbound cash box is earlier than or equal to the order requirement date. If the verification passes, the cash box is shipped out and marked as "shipped"; if the verification fails, the outbound process is frozen and an exception report is generated. Preferably, the re-deposit operation is performed by automated transmission equipment, for example, the cash boxes are stored in the designated storage location in the order of timestamps.

[0064] Through these technical means, the task status flow mechanism connects the unpalletizing, combining, and dispatching operations into a closed-loop process. Dynamic updates of timestamp data and multi-level verification ensure strict control of each link. Timestamp status markings and operation logs provide a complete data link for inventory traceability, while embedded verification rules effectively prevent process interruptions caused by batch confusion or data inconsistencies, thereby ensuring the reliability and auditability of the warehouse picking and allocation process.

[0065] In general, the present invention drives inventory classification, picking logic and task status flow through timestamp data to achieve efficient and accurate processing of transfer orders. Generate and verify timestamps based on the warehousing date, store and execute first-in-first-out rules according to timestamps and box attributes; filter the inventory range that matches the timestamp after parsing the order requirements, and implement dynamic picking strategies of whole pallet priority, loose pallet replenishment and mixed pallet splitting in layers; inherit the original timestamp after pallet removal for differentiated combined pallet processing, and control the sequential flow of pallet removal, combined pallet and outbound delivery processes through the task state machine. Timestamp data runs through the entire inventory management process to ensure batch traceability, inventory status consistency and task execution controllability of picking operations, thereby improving the accuracy and reliability of collaborative warehouse operations.

[0066] In order to better understand the present invention, the above method is described in detail below with reference to specific examples.

[0067] Example 1: A provincial branch needs to execute a transfer order with the following requirements: Material name: RMB issuance fund; Coupon type: 100 yuan coupon; Type: sealed coupons (marked as A1 box), uncleared complete coupons (marked as A2 box); Total demand: 5 pallets (100,000 pieces) of sealed 100-yuan notes, 3 pallets (60,000 pieces) of loose uncleared notes. Current inventory timestamp distribution: Sealed notes: Batch 20230505 (3 pallets), Batch 20230507 (4 pallets); Uncleared complete coupons: Batch 20230506 (10 boxes on loose pallet), Batch 20230507 (1 mixed pallet containing 5 boxes of this type of coupon).

[0068] The steps of the process in this embodiment are as follows: 1. Order review and timestamp screening: After the warehouse manager selects the same-day delivery order, the system filters the inventory by timestamp ≤ 20230507: Sealed coupons: batches 20230505 and 20230507; Uncleared complete coupons: batches 20230506 and 20230507; Calculate the full container requirement: 5 pallets are required for sealed coupons (insufficient full pallets will trigger a shortage alarm), and there is no full container requirement for uncleared coupons.

[0069] 2. Timestamp-driven picking logic: Sealed coupon selection: Priority is given to calling the 3 pallets with the earliest timestamp 20230505, and the remaining 2 pallets are called from the 20230507 batch.

[0070] The recorded picking results are: 20230505 (3 pallets), 20230507 (2 pallets).

[0071] Uncleared complete coupons selection: ① Priority is given to calling 3 boxes of 20230506 batch of loose pallets (the remaining demand is 0 boxes); ② If the demand is not met, split the 5 boxes in the mixed pallet batch 20230507 and store them in the box library with the original timestamp.

[0072] 3. De-trusting and timestamp inheritance: The sealed pallets are directly sent to the inspection area, and the timestamps remain the original batch (20230505, 20230507); After the unsorted vouchers are mixed and split, the 5 boxes taken out are marked as the batch of May 7, 2023. When combined into one pallet according to Rule B, a timestamp mapping table is generated: {Combined pallet ID: HT001, Timestamp list: [20230507], Denomination: Unsorted complete vouchers of 100 yuan} 4. Outbound and Timestamp Traceability: Same-day outbound: The whole pallet of original-sealed vouchers and the combined pallet of unsorted vouchers are out of the warehouse. The system records the outbound timestamp as the operation time (such as 20230507 - 14:30); In the inventory table, the batch of original-sealed vouchers on May 5, 2023 is marked as "out of the warehouse", the loose pallet of unsorted vouchers on May 6, 2023 is reduced by 3 boxes, and the record of the mixed pallet split on May 7, 2023 is retained.

[0073] Example 2: Transfer order number: 9990192303071561 Requirement content: 120,000 pieces of 50-yuan RMB issue fund (2005 version) in paper of X00023229 (6 whole pallets are required, 20,000 pieces per pallet); 180,000 pieces of 100-yuan RMB issue fund (2015 version) in paper of XX00023278 (9 whole pallets are required, 20,000 pieces per pallet).

[0074] Inventory structure (timestamp priority order: 20230501 <2023050>5 <2023050>7): 50-y-y]]-yuan vouchers: PPA area (whole pallet warehouse): Batch of May 1, 2023 (4 pallets), Batch of May 5, 2023 (3 pallets); PMA area (loose pallet warehouse): Batch of May 7, 2023 (5 loose boxes, 4,0 ,000 pieces per box).

[0075] 100-yuan vouchers: PPA area (whole pallet warehouse): Batch of May 1, 2023 (7 pallets); Mixed pallet area: Batch of May 5, 2023 (the mixed pallet contains 10 boxes of 100-yuan vouchers).

[0076] The process execution steps of this example are as follows: 1. Inventory screening and timestamp priority determination The system preferentially calls unmarked inventory and screens according to the timestamp order: 50-yuan vouchers: Batch of May 1, 2023 (4 whole pallets in PPA) → Meet the demand for 4 pallets The remaining demand for 2 pallets triggers the greedy algorithm to call the batch of May 5, 2023 (3 whole pallets in PPA) → Take 2 pallets; 100 yuan coupon: Batch 20230501 (PPA full pallet of 7 pallets) → less than 9 pallets required, triggering timestamp +1.

[0077] 2. Timestamp-driven hierarchical picking: 100 yuan coupon processing process: 1) Failed to sort the entire pallet (inadequate batch size 20230501) → Entering the loose pallet process: Check the mixed pallet batch 20230505 (10 cartons, 20,000 sheets / carton): Mixed pallets are directly used without unpacking (10 boxes = 200,000 sheets) → 20,000 sheets are exceeded; The system rejects the call (does not meet the exact matching rules).

[0078] 2) Forced removal operation: Split 2 boxes (40,000 pieces) from the mixed pallet batch 20230505 → store them in the box library with the original timestamp Record the unpalletizing log: {unpalletizing timestamp: 20230505, inherited batch: [20230505], number of unpalletized boxes: 2} 3. State transfer and timestamp update: The removal of the support is completed: The two boxes of 100-yuan notes removed are marked as "removed and waiting to be re-removed", and the timestamp is still 20230505 Code support operation: Combine the 2 removed boxes with the 7th pallet of batch 20230501 in the PPA area to generate a new pallet record: {Co-trust ID: HT100_202305, timestamp list: [20230501, 20230505], coupon type: 100 yuan (2015 edition)}.

[0079] 4. Outbound delivery and traceability verification: 50-yuan coupons: directly issue batches 20230501 and 20230505, with the original timestamps retained; 100 yuan coupon: Outbound shipment HT100_202305, system record: Original batch: 20230501 (7 pallets) + 20230505 (2 boxes of unpallets); Outgoing timestamp: 20230507-09:30.

[0080] Inventory status synchronization: Batch 20230501 is marked as "shipped"; There are 8 boxes left in the mixed pallet of batch 20230505, and the status is updated to "partially unpalletized".

[0081] The warehouse multi-factor feedback mechanism collaborative picking system described below and the warehouse multi-factor feedback mechanism collaborative picking method described above can be referenced to each other.

[0082] Please see the attached Figure 2 The present invention also provides a warehouse multi-factor feedback mechanism collaborative picking system, including: The timestamp generation module 10 is used to generate and verify timestamp data for the incoming payment box; The classification storage module 20 classifies the cash boxes by material name, ticket type, type, and mark based on the timestamp data, and stores them in order by timestamp; Dynamic picking module 30, which calls the full pallet, loose pallet and mixed pallet inventory in timestamp order and uses a greedy algorithm to optimize the picking path; The splitting and combining module 40 retains the original timestamp data when splitting mixed pallet inventory and performs classified combining operations; The task status management module 50 updates the status of the depalletizing, consolidating and shipping tasks based on the timestamp data.

[0083] The device of this embodiment can be used to execute the above method embodiment, and its principles and technical effects are similar, so they will not be repeated here.

[0084] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A warehouse multi-factor feedback mechanism collaborative picking method, characterized by: The following steps are involved: S1. Generate timestamp data for the incoming payment box and verify the integrity and uniqueness of the timestamp; the timestamp data is generated based on the incoming payment date; S2. Based on the timestamp data, the cash boxes are classified and stored in a designated area according to material name, ticket type, type, and mark, and cash boxes with the same timestamp are sorted according to the first-in-first-out rule; S3. Analyze transfer order requirements, break them down into full-case and bulk-case requirements, and filter inventory ranges based on timestamp data. S4. Verify that inventory meets demand. If inventory is insufficient, provide feedback on inventory shortfall information with specific coupon type and timestamp. S5. Call inventory resources in timestamp order and perform picking operations in sequence: whole pallet priority, loose pallet replenishment, mixed pallet splitting, and update picking results based on timestamp data. S6. The unpacked cash boxes are collocated. The collocation rules perform differentiated operations based on the timestamp data and cash box type. S7. The process of unpalletizing, combining and shipping out is controlled through the task status transfer mechanism. The status transfer depends on the update and verification of the timestamp data.

2. The warehouse multi-factor feedback mechanism collaborative picking method according to claim 1 is characterized in that: In the step S1, the generation of the timestamp is linked to the storage device through the interface, written into the database in real time, and the format validity of the timestamp is verified.

3. The warehouse multi-factor feedback mechanism collaborative picking method according to claim 1 is characterized in that: In the step S2, the classified storage includes: The sealed coupons are stored separately according to the timestamp and coupon type; Non-sealed coupons are stored mixedly according to timestamps, and the storage location is bound to the timestamp data.

4. The warehouse multi-factor feedback mechanism collaborative picking method according to claim 1 is characterized in that: In step S4, the feedback of inventory shortage information of specific coupon types and timestamps includes: The type of insufficient stock, timestamp, and quantity of the shortfall; Recommended replenishment timestamp range.

5. The warehouse multi-factor feedback mechanism collaborative picking method according to claim 1 is characterized in that: In the step S5, when whole pallets are picked first, the whole pallet box with the earliest timestamp is randomly selected, and the picking timestamp range is recorded.

6. The warehouse multi-factor feedback mechanism collaborative picking method according to claim 1 is characterized in that: In step S5, the mixed-support splitting operation includes: When splitting mixed cash boxes, retain the original timestamp data; The split cash boxes are stored back in the box library and marked as the inventory corresponding to the original timestamp.

7. The warehouse multi-factor feedback mechanism collaborative picking method according to claim 1 is characterized in that: In the step S5, when the loose pallet is supplemented and picked, a greedy algorithm is used to combine the number of loose pallet boxes, and the input parameters of the greedy algorithm include the timestamp priority and the combination of the number of loose pallet boxes.

8. The warehouse multi-factor feedback mechanism collaborative picking method according to claim 1 is characterized in that: In step S6, the joint trusteeship rule is: The sealed coupons are recorded in separate consignment records according to the timestamp and coupon type; When non-sealed bills are combined and consigned together, a mapping table between timestamps and consignment results is generated.

9. The warehouse multi-factor feedback mechanism collaborative picking method according to claim 1 is characterized in that: In step S7, the task status flow includes: After the removal task is completed, the timestamp data is updated; After the consignment task is completed, the transfer order is bound and marked as picked.

10. A warehouse multi-factor feedback mechanism collaborative picking system, used to execute the method according to any one of claims 1 to 9, characterized in that: include: Timestamp generation module, used to generate and verify timestamp data for incoming payment boxes; The classification storage module classifies cash boxes by material name, voucher type, type, and mark based on timestamp data, and stores them in order by timestamp; The dynamic picking module calls the full pallet, loose pallet, and mixed pallet inventory in timestamp order and uses a greedy algorithm to optimize the picking path; The splitting and combining module retains the original timestamp data when splitting mixed pallet inventory and performs classified combining operations; The task status management module relies on timestamp data to update the status of depalletizing, consolidating and outbound tasks.

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