A multi-category goods warehousing intelligent traceability scheduling method based on an internet of things

Abstract

The present application relates to the technical field of warehouse logistics, Internet of Things sensing, data tracing and intelligent scheduling, and discloses a multi-category goods warehouse intelligent tracing and scheduling method based on the Internet of Things. The method binds a unique Internet of Things identity to the goods entering the warehouse, collects multi-source data such as the category, batch, temperature and humidity sensitivity level, shelf life, hazardous / easily broken / valuable attributes, storage location environment, handling equipment status, order timeliness and operation personnel permissions of the goods, and constructs a multi-dimensional correlation graph of goods-storage location-equipment-task. Further, category compatibility constraints, environment adaptation constraints, first-in-first-out / short-shelf-life-priority constraints, path congestion constraints and abnormal risk constraints are established, and the tracing credible score and scheduling benefit function are used for dynamic convergence solution to generate linkage scheduling instructions for warehouse shelving, warehouse relocation, picking, review, warehouse dispatching and abnormal disposal. At the same time, key node data is written into the non-tamperable tracing chain to form the goods flow track.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehousing and logistics technology, specifically to an intelligent traceability and scheduling method for multi-category goods warehousing based on the Internet of Things. Background Technology

[0002] With the development of supply chain digitalization and the demand for just-in-time fulfillment, warehouses are no longer just static storage facilities. They are gradually evolving into high-frequency operational nodes integrating receiving, quality inspection, zoned storage, replenishment, picking, verification, packaging, distribution, returns, and quality accountability. Especially in mixed warehousing scenarios with multiple product categories, different goods have significant differences in terms of temperature, humidity, stacking, isolation, safety supervision, expiration dates, batch turnover, and order fulfillment timelines. For example, food and chemicals cannot be stored together, cold chain products require continuous temperature control, pharmaceuticals require precise tracking of batch numbers and expiration dates, valuable goods require access control and tracking, and fragile items require restrictions on handling equipment and stacking layers.

[0003] Existing warehouse management systems typically revolve around inbound and outbound orders and inventory ledgers, enabling basic inventory management. However, they still fall short in areas such as complex constraints across multiple product categories, real-time IoT status monitoring, reliable traceability of goods flow, and dynamic scheduling and coordination. While some systems incorporate RFID or barcode recognition, these are usually only used for inbound and outbound scanning, lacking unified modeling of the relationships between warehouse environment, equipment status, aisle congestion, batch risk, and order waves. Furthermore, some automated warehousing systems focus on AGV, stacker crane, or conveyor line scheduling, but their scheduling objectives are often concentrated on the shortest path or highest equipment utilization, failing to incorporate product category compatibility, quality risk, prioritization of near-expiration dates, and traceability reliability into unified decision-making.

[0004] In practical applications, relying solely on manual experience or single rules for shelving and picking can easily lead to problems such as uneven storage space utilization, dispersed batches, retention of near-expiry goods, environmental violations of sensitive goods, difficulty in defining the scope of impact of abnormal goods, and overlapping order picking paths. When damage, temperature control abnormalities, mispicking, mixed batches, or regulatory spot checks occur, traditional systems often require manual querying of multiple system logs, making it difficult to quickly confirm the time, location, responsible party, and scope of affected goods, thereby increasing recall costs and fulfillment risks.

[0005] Therefore, it is necessary to propose a technical solution that integrates real-time IoT sensing, full lifecycle traceability of goods, multi-category constraint modeling, and intelligent scheduling optimization. This solution enables the warehousing system to not only record where the goods are, but also to determine where the goods should be placed, what path and resources should be used for operations, and how to trace and reschedule in case of anomalies. This will improve the safety, efficiency, and traceability of multi-category warehousing. To this end, an IoT-based intelligent traceability and scheduling method for multi-category goods warehousing is proposed. Summary of the Invention

[0006] In view of this, the present invention provides an intelligent traceability and scheduling method for multi-category goods warehousing based on the Internet of Things, in order to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial option.

[0007] The technical solution of this invention is implemented as follows: A multi-category goods warehousing intelligent traceability and scheduling method based on the Internet of Things (IoT), comprising the following steps: S1, generating or reading a unique IoT identity identifier for goods to be received, and collecting the category attributes, batch attributes, quality attributes, packaging attributes, storage environment requirements, expiration date attributes, and regulatory attributes of the goods; S2, collecting real-time status data of warehouse locations, shelves, aisles, handling equipment, sensors, operators, and order tasks to form a warehousing IoT status dataset; S3, based on the unique IoT identity identifier of the goods, uniformly encoding the goods attribute data, receiving detection data, warehouse location environment data, operation event data, and order association data to construct a goods full lifecycle traceability data unit; S4, based on the compatibility relationship between goods categories, the warehouse location environment matching relationship, ... S5. Generate a multi-category warehousing constraint model based on batch turnover rules, order timeliness requirements, and warehouse operation resource status; S6. Calculate a comprehensive scheduling score for candidate storage locations, candidate paths, and candidate operation resources based on the traceability data unit and warehousing constraint model; S7. Generate an intelligent scheduling scheme for inbound shelving, in-warehouse relocation, order picking, verification and packaging, outbound handover, and anomaly handling based on the comprehensive scheduling score; S8. Distribute the scheduling scheme to warehousing execution equipment or operation terminals, and collect execution events in real time during execution; S9. Trigger an anomaly traceability and rescheduling process when the execution event deviates from the preset scheduling scheme, environmental threshold, or quality threshold; S10. Write key events in the inbound, storage, relocation, picking, verification, outbound, and anomaly handling of goods into the traceability chain to form a queryable, verifiable, and traceable warehousing traceability record.

[0008] More preferably, the unique IoT identity includes RFID tags, QR codes, NFC tags, Bluetooth Low Energy beacons, UWB positioning tags, or combinations thereof, and the unique IoT identity is associated with at least the goods code, batch code, supplier code, warehouse entry code, packaging unit code, and parent-child packaging hierarchy.

[0009] More preferably, the category attributes include at least one classification label from general merchandise, cold chain merchandise, temperature and humidity controlled merchandise, fragile merchandise, valuable merchandise, hazardous chemicals, food, pharmaceuticals, electronic components, or clothing, and a main category label and at least one additional risk label are allowed to be configured for the same goods.

[0010] Further preferably, the multi-category warehousing constraint model includes: category isolation constraint, temperature and humidity adaptation constraint, near-expiration priority constraint, first-in-first-out constraint, same-batch concentration constraint, order wave association constraint, storage location capacity constraint, goods stacking constraint, aisle congestion constraint, equipment power constraint, and personnel access control constraint.

[0011] More preferably, the comprehensive scheduling score is calculated through at least three dimensions: the first dimension is the spatial value dimension, which is used to characterize the space utilization rate of candidate storage locations, the compatibility of adjacent goods, and the storage location turnover efficiency; the second dimension is the timeliness value dimension, which is used to characterize the validity period of goods, the order delivery time limit, the picking path distance, and the availability of operating equipment; and the third dimension is the reliable traceability value dimension, which is used to characterize the success rate of goods identification reading, the integrity of sensor data, the consistency of operating nodes, and the risk of historical anomalies.

[0012] More preferably, the comprehensive scheduling score F satisfies: F=αA+βB+γC-δR, where A is the spatial value score, B is the timeliness value score, C is the credible traceability value score, R is the abnormal risk penalty item, and α, β, γ, and δ are dynamic weights; the dynamic weights are adaptively adjusted according to warehouse load, order urgency, category risk level, and environmental fluctuations.

[0013] More preferably, the anomaly tracing and rescheduling process includes: identifying the anomaly type; tracing back the identity identifier, batch, storage location, sensor, and operation event corresponding to the anomaly goods; determining the scope of the anomaly's impact; locking the affected goods, storage locations, and orders; generating a rescheduling plan for isolation, re-inspection, transfer, replacement picking, or suspension of outbound shipment; and writing the anomaly cause, handling measures, and responsible nodes into the tracing record.

[0014] More preferably, the traceability chain uses a chained data structure composed of hash digests, timestamps, event signatures, and pointers to previous and subsequent events. Each key event includes the event subject, event object, event time, event location, equipment number, operator number, sensor data digest, and business document number.

[0015] More preferably, the intelligent scheduling scheme is simulated and verified using a digital twin warehouse model before execution. The digital twin warehouse model includes at least the shelf layout, storage capacity, aisle topology, equipment location, goods location, environmental zoning, and task queue.

[0016] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions:

[0017] I. This invention binds a unique Internet of Things (IoT) identity to the parent-child packaging hierarchy, enabling hierarchical traceability from pallets, boxes, bags, and pieces to order details, thus avoiding identity breakage after multiple splitting, merging, and repackaging.

[0018] Second, this invention uses unified constraint modeling based on category compatibility, environment adaptation, batch turnover, order timeliness, and equipment status to make warehouse location allocation and operation scheduling no longer rely on a single empirical rule, and can adapt to the complex requirements of multi-category mixed warehousing.

[0019] Third, this invention uses a comprehensive scoring mechanism that considers spatial value, timeliness value, and credible traceability value, enabling the system to improve space utilization and operational efficiency while also taking into account quality, safety, and accountability.

[0020] Fourth, by performing real-time data collection and rescheduling during the execution process, this invention can quickly determine the scope of impact and generate a solution in cases of excessive temperature and humidity, incorrect picking, missed scanning, path congestion, equipment failure, etc.

[0021] Fifth, this invention improves the integrity, tamper resistance, and audit credibility of warehouse data by recording key events through a chain-like evidence storage structure.

[0022] VI. This invention reduces scheduling conflicts and improves the stability of automated equipment and human collaboration through digital twin simulation verification.

[0023] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of the overall process of the present invention;

[0026] Figure 2 This is a system module structure diagram of the present invention;

[0027] Figure 3 This is a diagram of the multi-category constraint model of the present invention;

[0028] Figure 4 This is a flowchart of the anomaly tracing and rescheduling process of the present invention;

[0029] Figure 5 This is a data structure diagram of the traceability chain of this invention. Detailed Implementation

[0030] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0031] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0032] like Figure 1-5 As shown,

[0033] I. IoT Identity Binding Process

[0034] During the goods pre-receipt stage, the system generates a pre-receipt record based on the purchase order, supplier delivery note, or production batch order. Upon arrival at the warehouse, the system reads the goods' existing identification using RFID readers, barcode scanners, NFC terminals, UWB positioning base stations, or mobile PDAs. If the goods lack an identifiable identifier, the system generates a unique IoT identity and writes it to an RFID tag or prints a QR code label. For goods with multi-level packaging, the system establishes a parent-child hierarchy between pallets, turnover boxes, inner boxes, and individual items, allowing traceability back to the original batch even after unpacking, unpacking, repackaging, and re-packaging.

[0035] When binding an identity, the system must collect at least the following fields: product name, SKU code, batch number, production date, expiration date, supplier, warehouse receipt, packaging specifications, quantity, weight, volume, storage temperature range, storage humidity range, whether it is fragile, whether it is valuable, whether it is a hazardous material, whether it requires regulatory traceability, and whether mixed storage is permitted. For regulated categories such as pharmaceuticals, food, and hazardous chemicals, the system can further collect the license number, inspection report number, transportation temperature control record, and regulatory code.

[0036] II. Multi-source data acquisition and standardization process

[0037] The sensors and equipment deployed within the warehouse include temperature and humidity sensors, light sensors, smoke sensors, door magnetic sensors, cameras, RFID access gates, UWB base stations, AGVs, forklifts, conveyor lines, electronic shelf labels, and handheld terminals. The system integrates the raw data collected by these devices into a unified data gateway and parses data using different protocols, including MQTT, HTTP, Modbus, OPCUA, Bluetooth, ZigBee, or proprietary protocols.

[0038] Data standardization includes field mapping, unit conversion, timestamp calibration, outlier removal, duplicate data merging, and missing value marking. For example, Celsius, relative humidity, storage location coordinates, equipment power, and task status are uniformly converted into standard fields within the system. When multiple sensors monitor the same storage area simultaneously, a confidence-weighted method is used to generate the storage area's environmental status. Confidence scores are set for key data; if RFID reading fails but QR code reading succeeds, the identity credibility is reduced but the process is not directly interrupted; if the identity identifier, batch, and quantity are inconsistent, manual review or anomaly handling is initiated.

[0039] III. Construction of Cargo Lifecycle Traceability Data Units

[0040] The system uses a unique IoT identifier as the primary key to construct a data unit for tracing the entire lifecycle of goods. This data unit includes a static attribute area, a dynamic status area, an operational event area, an environmental evidence area, an order association area, and an anomaly handling area. The static attribute area stores the goods category, batch, supplier, packaging, and regulatory fields; the dynamic status area stores the current location, inventory status, lock status, quality status, and available quantity; the operational event area stores events such as warehousing, quality inspection, shelving, transfer, inventory counting, picking, verification, packaging, and outbound; the environmental evidence area stores temperature, humidity, and other environmental summaries associated with the storage location of the goods; the order association area stores sales orders, transfer orders, return orders, or production material requisition orders; and the anomaly handling area stores the cause of the anomaly, its scope of impact, handling actions, and responsible nodes.

[0041] IV. Establishment of a Multi-Category Warehousing Constraint Model

[0042] The multi-category warehousing constraint model is used to determine whether a particular item is suitable for storage in a specific location, whether it is suitable for coexistence with adjacent items, whether it is suitable for operation by a specific piece of equipment, and whether it is suitable for inclusion in a specific order wave. The model includes hard constraints and soft constraints. Hard constraints are those that cannot be violated, such as the segregation of hazardous materials, the segregation of food from goods at risk of contamination, temperature control mismatch, insufficient storage space capacity, and insufficient personnel permissions. Soft constraints are those that can be reflected through scoring and penalties, such as batch concentration, proximity to the outbound exit, path congestion, picking wave matching, and the popularity of the goods.

[0043] In one implementation, the system establishes a category compatibility matrix M, where M(i,j)=1 indicates that category i and category j are allowed to be stored adjacently or in the same area, M(i,j)=0 indicates that adjacent or same-area storage is prohibited, and M(i,j) between 0 and 1 indicates that storage is allowed but the score needs to be reduced. The system also establishes an environmental adaptation function E(g,l) to calculate the degree of matching between goods g and storage location l in terms of temperature, humidity, light, and cleanliness; establishes a turnover priority function T(g,o) to calculate the outbound priority of goods g for order o based on expiration date, first-in-first-out, and order timeliness; and establishes a resource availability function R(t,e) to describe the degree of matching between task t and equipment e and personnel p.

[0044] V. Comprehensive Scheduling Scoring and Scheme Generation

[0045] For each item to be shelved, the system generates several candidate storage locations; for each order task to be executed, the system generates several candidate items, candidate routes, and candidate equipment. The system calculates the spatial value score A, timeliness value score B, reliable traceability value score C, and anomaly risk penalty item R, respectively, and obtains the comprehensive scheduling score F=αA+βB+γC-δR. The spatial value score A comprehensively considers the remaining storage location capacity, volume matching degree, weight capacity, category adjacency compatibility, concentration of the same batch, and the priority of best-selling items; the timeliness value score B comprehensively considers the order time limit, picking distance, equipment availability time, route congestion, and priority of near-expiration dates; the reliable traceability value score C comprehensively considers the completeness of identity reading, sensor coverage, number of historical anomalies, completeness of event chains, and consistency of personnel permissions; the anomaly risk penalty item R comprehensively considers temperature and humidity fluctuations, historical anomalies of storage locations, probability of equipment failure, risk level of goods, and risk of mixed storage.

[0046] When warehouse load is low, the system can increase the weight of space value to optimize storage location layout; when orders are nearing delivery, the system increases the weight of timeliness value; when goods are food, medicine, hazardous chemicals, or high-value goods, the system increases the weight of credible traceability value and risk penalty weight. The system selects candidate solutions from high to low based on the comprehensive scheduling score and verifies path conflicts, equipment conflicts, storage location conflicts, and operation sequence conflicts in the digital twin warehouse model. After verification, operation instructions are generated and sent to AGVs, forklift terminals, electronic tags, conveyor control systems, or operator PDAs.

[0047] VI. Perform data recovery, anomaly tracing, and rescheduling.

[0048] During operation, the system collects real-time data on equipment location, task progress, barcode scanning results, RFID reading results, warehouse environment, and manual confirmation information. If an anomaly occurs, the system first determines the anomaly type, including inconsistencies in identity, quantity discrepancies, incorrect warehouse location, exceeding temperature and humidity limits, equipment malfunction, path congestion, order timeliness risks, damaged goods, batch confusion, or access violations. Subsequently, the system uses the goods' identification identifier as the entry point to trace back the associated batch, warehouse location, sensors, equipment, personnel, and order, calculating the scope of the anomaly's impact. For example, when temperature and humidity exceed limits, the system identifies the affected warehouse area, the time period of the exceedance, goods in the same warehouse area, and goods already shipped; when a wrong pick occurs, the system identifies the incorrect goods, the affected order, alternative inventory, and the responsible verification point.

[0049] The system generates rescheduling plans based on the anomaly type: In case of temperature control anomalies, affected goods are moved to backup temperature-controlled storage locations and re-inspected; in case of equipment failure, unfinished tasks are reassigned to other equipment or personnel; in case of incorrect picking or missed scanning, orders are locked and verification and replacement picking are triggered; when goods nearing their expiration date are delayed, picking priorities are adjusted and promotional or transfer prompts are issued; in case of hazardous chemical isolation anomalies, relevant storage locations are immediately locked and outbound shipments are prohibited. After anomaly handling is completed, the system writes the anomaly occurrence, judgment criteria, handling actions, verification results, and responsible nodes into the traceability chain.

[0050] VII. Traceability Chain Evidence Preservation and Auditing

[0051] The traceability chain uses a chain-like data structure to store key events. Each event record includes an event number, cargo identification, event type, event time, event location, equipment number, personnel number, business document number, sensor data summary, previous event hash, current event hash, and event signature. The current event hash is calculated by combining the previous event hash and the current event data, ensuring that any tampering with historical events will cause subsequent hash verifications to fail. The query and audit module supports searching by cargo, batch, order, storage location, supplier, anomaly type, or time interval, and outputs cargo flow trajectory, environmental curve summary, responsible node, and scope of impact.

[0052] Example

[0053] Example 1: Warehousing and Shelving Scheduling of Mixed Warehouses for Food, Daily Chemicals, and Electronic Components

[0054] A mixed warehouse stores food, daily necessities, and electronic components. Food requires a temperature of 15-25 degrees Celsius and a relative humidity of 40%-65%, and must not be stored near strongly scented daily necessities. Electronic components require a relative humidity below 55% and should be kept away from high-humidity areas. Upon arrival, the system reads the pallet RFID and collects the box code, establishing a pallet-box-item hierarchy. The system identifies batch A of food as having 30 days remaining until its expiration date, classifying it as near-expiration; batch B of daily necessities is a strongly scented category; and batch C of electronic components is a humidity-sensitive category.

[0055] The system uses a category compatibility matrix to exclude adjacent storage locations for food and strongly scented daily necessities, and an environmental adaptability function to exclude candidate storage locations for electronic components with high humidity. Based on a near-expiration date priority rule, batch A food is allocated to a storage location close to the food exit and easily accessible for picking. Ultimately, batch A food is placed in the food turnover area, batch B daily necessities are placed in the isolated daily necessities area, and batch C electronic components are placed in the low-humidity electronics area. All shelving events, storage location environmental summaries, and operator information are written into the traceability chain.

[0056] Example 2: Traceability and Rescheduling of Cold Chain Goods Under Abnormal Temperature Conditions

[0057] A batch of dairy products (Batch D) in a cold chain warehouse requires storage at 2-8 degrees Celsius. The system detected, via temperature sensors, that the temperature in cold storage area L2 rose to 11 degrees Celsius within 10 minutes. The anomaly tracing and rescheduling module automatically identified this as an environmental violation, traced all goods stored in storage area L2 during the period of violation, and identified batch D of dairy products and two other batches of food in the same temperature zone. The system then locked the relevant batches, suspended their outbound shipment, and searched for alternative cold storage locations.

[0058] The scheduling and calculation module comprehensively considers the temperature stability, distance, capacity, and equipment availability of the backup storage location to generate a transfer task. This task moves batch D of dairy products to the L5 backup cold storage location, while simultaneously issuing a re-inspection task to the quality inspection personnel's terminal. If the re-inspection is successful, the system unlocks the inventory and records the duration of the temperature anomaly; if the re-inspection fails, the system marks the batch as unsaleable and generates a recall or loss reporting process. This embodiment demonstrates that the present invention can quickly determine the scope of impact and implement closed-loop management after an environmental anomaly occurs.

[0059] Example 3: Prioritizing near-delivery picking in multi-order waves

[0060] A food warehouse received multiple online orders, all containing beverages of the same SKU. After checking the inventory, the system found three batches of the same SKU with remaining expiration dates of 15 days, 45 days, and 90 days, respectively. A traditional system might select the 45-day batch based on proximity, while this invention incorporates near-expiration date priority and first-in-first-out (FIFO) rules into the timeliness value scoring, prioritizing the 15-day batch without affecting order fulfillment timelines.

[0061] The system merges multiple orders into waves, selecting the nearest-expiration batches as the primary inventory. When near-expiration inventory is insufficient, it automatically replenishes 45-day batches. During picking, operators scan the goods' identification tags with their PDAs. After the system verifies that the order, batch, and quantity match, it allows for repackaging. If a 90-day batch is scanned, the system indicates that the batch does not meet the priority rules and requests a replacement. This reduces near-expiration inventory retention and improves batch turnover efficiency.

[0062] Example 4: Equipment failure leads to path rerouting

[0063] In the automated warehouse, AGV-03's battery level fell below a threshold while performing a transport task from storage location K12 to verification station R2, triggering a fault risk report. Upon receiving the device status, the system set AGV-03's incomplete task to "pending rescheduling," queried nearby available devices AGV-05 and AGV-06, and verified aisle congestion and intersecting paths in the digital twin warehouse model. The system found that AGV-05 was closer but its path passed through a congested aisle, while AGV-06 was slightly farther but its path was clear and its battery was sufficient. After comprehensive evaluation, AGV-06 was selected to perform the task.

[0064] The system writes the original path, rearrangement reason, new device number, and task handover time into the traceability chain. Because the scheduling scheme considers device battery power, path congestion, order deadlines, and cargo identity consistency, it can avoid order delays or cargo trajectory breaks caused by device failures.

[0065] Example of technical effect verification

[0066] Multi-category warehouse location allocation Relying on human experience or fixed partitions Dynamic allocation based on compatibility matrix and environment adaptation function Reduce the risk of mixed storage and environmental incompatibility Near-expiry date for shipment Partially relies on manual screening Automatically combines first-in-first-out and order wave scoring Reduce near-expiration period Anomaly tracing Manually query multiple system logs Use identity identifiers to trace batches, storage locations, equipment, and orders. shorten positioning time Equipment scheduling Shortest path or manual dispatch Comprehensive equipment status, channel congestion, and traceability integrity Improve contract performance stability Data trust Ordinary database records Hash chain event storage Enhance auditing and accountability capabilities

[0067] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligent traceability and scheduling of multi-category goods warehousing based on the Internet of Things, characterized in that, The process includes the following steps: S1. Generating or reading a unique IoT identity for goods to be received, and collecting the goods' category attributes, batch attributes, quality attributes, packaging attributes, storage environment requirements, expiration date attributes, and regulatory attributes; S2. Collecting real-time status data of warehouse locations, shelves, aisles, handling equipment, sensors, operators, and order tasks to form a warehouse IoT status dataset; S3. Based on the unique IoT identity of the goods, uniformly encoding the goods attribute data, receiving inspection data, warehouse location environment data, operation event data, and order-related data to construct a goods lifecycle traceability data unit; S4. Generating a multi-category warehouse constraint model based on the compatibility relationships between goods categories, warehouse location environment matching relationships, batch turnover rules, order timeliness requirements, and warehouse operation resource status; S5. Calculating a comprehensive scheduling score for candidate warehouse locations, candidate paths, and candidate operation resources based on the traceability data unit and the warehouse constraint model. S6. Generate intelligent scheduling solutions for inbound shelving, in-warehouse relocation, order picking, verification and packaging, outbound handover, and exception handling based on comprehensive scheduling scores; S7. Distribute the scheduling plan to the warehouse execution equipment or operation terminal, and collect execution events in real time during the execution process; S8. When the execution event deviates from the preset scheduling scheme, environmental threshold or quality threshold, the anomaly tracing and rescheduling process is triggered. S9. Key events in the process of goods entering the warehouse, storing, transferring, picking, verifying, leaving the warehouse, and handling abnormalities are written into the traceability chain to form a queried, verifiable, and accountable warehouse traceability record.

2. The method according to claim 1, characterized in that, The unique IoT identity includes RFID tags, QR codes, NFC tags, Bluetooth Low Energy beacons, UWB positioning tags, or combinations thereof. The unique IoT identity is associated with at least the goods code, batch code, supplier code, warehouse entry code, packaging unit code, and parent-child packaging hierarchy.

3. The method according to claim 1, characterized in that, The category attributes include at least one classification label from general merchandise, cold chain merchandise, temperature and humidity controlled merchandise, fragile merchandise, valuable merchandise, hazardous chemicals, food, pharmaceuticals, electronic components, or clothing, and a main category label and at least one additional risk label are allowed to be configured for the same goods.

4. The method according to claim 1, characterized in that, The multi-category warehousing constraint model includes: category isolation constraint, temperature and humidity adaptation constraint, near-expiration priority constraint, first-in-first-out constraint, same-batch concentration constraint, order wave association constraint, warehouse capacity constraint, goods stacking constraint, aisle congestion constraint, equipment power constraint, and personnel access constraint.

5. The method according to claim 1, characterized in that, The comprehensive scheduling score is calculated through at least three dimensions: the first dimension is the spatial value dimension, which is used to characterize the space utilization rate of candidate storage locations, the compatibility of adjacent goods, and the storage location turnover efficiency; the second dimension is the timeliness value dimension, which is used to characterize the validity period of goods, the order delivery time limit, the picking path distance, and the availability of operating equipment; and the third dimension is the reliable traceability value dimension, which is used to characterize the success rate of goods identification reading, the integrity of sensor data, the consistency of operating nodes, and the risk of historical anomalies.

6. The method according to claim 5, characterized in that, The comprehensive scheduling score F satisfies: F=αA+βB+γC-δR, where A is the spatial value score, B is the timeliness value score, C is the credible traceability value score, R is the abnormal risk penalty item, and α, β, γ, and δ are dynamic weights. The dynamic weights are adaptively adjusted based on warehouse load, order urgency, category risk level, and environmental fluctuations.

7. The method according to claim 1, characterized in that, The anomaly tracing and rescheduling process includes: identifying the anomaly type; tracing back the identity identifier, batch, storage location, sensor, and operation event corresponding to the anomaly goods; determining the scope of the anomaly's impact; locking the affected goods, storage locations, and orders; generating rescheduling plans for isolation, re-inspection, transfer, replacement picking, or suspension of outbound shipment; and writing the anomaly cause, handling measures, and responsible nodes into the tracing record.

8. The method according to claim 1, characterized in that, The traceability chain uses a chain-like data structure consisting of hash digests, timestamps, event signatures, and pointers to previous and subsequent events. Each key event includes the event subject, event object, event time, event location, equipment number, operator number, sensor data digest, and business document number.

9. The method according to claim 1, characterized in that, Before execution, the intelligent scheduling scheme is simulated and verified using a digital twin warehouse model. The digital twin warehouse model includes at least the shelf layout, storage capacity, aisle topology, equipment location, goods location, environmental zoning, and task queue.

Images