A steel storage logistics traceability and order intelligent matching and scheduling method

Abstract

The application discloses a steel storage logistics traceability and order intelligent matching scheduling method, comprising the following steps: S1, collecting the basic attribute data, quality certificate data and storage location data of the incoming steel, generating a unique digital identity for each batch of steel, thereby constructing a full life cycle digital traceability system for the steel, giving each batch of steel a digital identity and establishing a traceability file, effectively improving the supply chain transparency and fault tracing efficiency, digitally modeling the warehouse area, stacking position, loading and unloading equipment and platform, constructing a digital map of storage resources, dynamically updating the inventory, equipment and platform working status in combination with real-time operation data, planning the unloading, loading and transportation operation time sequence in combination with the order priority, inventory and logistics resources, eliminating operation disconnection, congestion, resource waste and other problems, and significantly improving the order delivery stability and scene adaptation ability.

Description

Technical Field

[0001] This application relates to the technical field of warehousing and logistics, and in particular to a method for tracing and intelligently matching and scheduling orders in steel warehousing and logistics. Background Technology

[0002] Steel warehousing and logistics spans the entire chain, from warehousing and storage to outbound transportation and final delivery. Its traceability capabilities, order matching accuracy, and scheduling coordination level directly impact the operating costs, delivery efficiency, and quality control effectiveness of the steel supply chain. With the industry's digital upgrade, the traditional manual-driven and fragmented information management model is no longer sufficient to meet the intelligent operation needs of steel warehousing and logistics.

[0003] Existing steel warehousing and logistics systems only record basic inbound and outbound transactions for traceability management. They fail to integrate basic steel attributes, quality certificates, storage locations, and full-process operational data, making it impossible to build a full lifecycle digital traceability archive. Data from different business segments is fragmented, and quality and logistics information cannot be linked for traceability. This makes it difficult to quickly locate problems such as mismatched goods, delivery deviations, and quality disputes. At the same time, customers cannot query batch and logistics information in real time.

[0004] Meanwhile, existing warehouse resource management is mostly static warehouse area management, which cannot update the available warehouse capacity, storage status, and busy / idle information of equipment and platforms in real time. Inventory data updates are delayed and the status is distorted, which can easily lead to wasted warehouse capacity, idle equipment, misuse of warehouse space and inventory deviation, and significantly reduce the overall utilization rate of warehouse resources.

[0005] In addition, the existing system suffers from fragmented operation links and chaotic timing. The outbound, loading and transportation operations lack coordinated planning, which easily leads to inefficiencies such as equipment waiting, vehicle delays and operation congestion. It is unable to identify sudden working conditions such as equipment failure, vehicle delays and abnormal road conditions in real time, making it difficult to achieve dynamic rescheduling and resulting in poor anti-interference ability for order delivery.

[0006] Application content

[0007] This application aims to address, at least to some extent, the technical problems in the related art.

[0008] To achieve the above objectives, this application proposes a method for steel warehousing and logistics traceability and intelligent order matching and scheduling, comprising the following steps:

[0009] S1. Collect basic attribute data, quality certificate data and storage location data of steel entering the warehouse, generate a unique digital identity for each batch of steel, and establish a traceability file for the entire life cycle of steel.

[0010] S2. Digitally model the storage area, stacking positions, loading and unloading equipment, and platform resources, and update the inventory quantity, status, and occupancy of steel in each stacking position in real time to form a dynamic inventory resource pool.

[0011] S3. Receive customer orders and perform structured parsing to extract steel demand parameters, delivery requirements and logistics parameters of the orders, and calculate order priority based on multi-dimensional evaluation indicators;

[0012] S4. Based on order priority, with the optimization goals of highest inventory matching degree, lowest outbound operation cost and best logistics transportation efficiency, select suitable inventory stacking locations from the dynamic inventory resource pool, match the corresponding loading and unloading resources and logistics capacity, and generate an initial order matching scheme.

[0013] S5. Based on the initial order matching scheme, combined with the time constraints of warehousing operations and logistics capacity, generate outbound operation sequence, loading operation plan and vehicle transportation route, and send them to the warehousing execution end and logistics execution end. During the execution process, collect abnormal information in real time and trigger dynamic rescheduling.

[0014] S6. Collect data on the entire order execution chain, including outbound, loading, in transit, and receipt nodes, and update the traceability files of the corresponding batch of steel. At the same time, feed the execution result data back to the matching and scheduling model to complete the model iteration and optimization.

[0015] Specifically, step S2 includes:

[0016] Based on the functional zoning of the storage area, the load-bearing limit of the stack, the suitable storage categories, and the rated load of the corresponding loading and unloading equipment, a digital map of storage resources is constructed, marking the types of steel that can be stored, the maximum storage capacity, and the corresponding loading and unloading equipment number for each stack.

[0017] Based on real-time data from inbound, transfer, and outbound operations, the system dynamically updates the steel inventory quantity, occupancy status, and available storage capacity at each storage location. It also synchronously updates the busy / idle status and available time periods of loading / unloading equipment and outbound platforms, forming a real-time dynamic inventory resource pool.

[0018] Specifically, in step S3, the steel demand parameters for the order include category, material, specifications, quantity, and processing requirements; delivery requirements include delivery time, delivery location, and acceptance standards; and logistics parameters include transportation method, vehicle requirements, and unloading conditions.

[0019] The multi-dimensional evaluation indicators include order delivery urgency, customer level, order amount, and logistics fulfillment difficulty. The weight of each indicator is determined by the analytic hierarchy process (AHP), and the order priority value is calculated. The higher the priority value, the higher the scheduling ranking.

[0020] Specifically, the order priority value P is calculated using the following formula:

[0021] P = W1·E + W2·C + W3·A + W4·L

[0022] Where E is the order delivery urgency score, C is the customer level score, A is the order amount score, L is the logistics fulfillment difficulty score, and W1, W2, W3, and W4 are the corresponding weights, which are determined by the analytic hierarchy process and satisfy W1+W2+W3+W4=1.

[0023] Specifically, the multi-target intelligent matching in step S4 includes:

[0024] Based on the steel category, material, specifications, and quantity requirements of the order, a set of candidate inventory locations with completely matching attributes is selected from the dynamic inventory resource pool;

[0025] For each candidate inventory location, calculate the outbound operation cost, logistics transportation distance, and estimated transportation time.

[0026] A multi-objective optimization function is constructed, with objective functions including maximizing inventory matching accuracy, minimizing overall operating costs, and minimizing order response time. Constraints include total inventory meeting order requirements, load matching of loading and unloading equipment, and matching of vehicle load and volume. An improved genetic algorithm is used to solve for the optimal matching scheme.

[0027] For orders that cannot be fulfilled by a single pallet position, they are automatically split into multiple suitable pallet positions. For multiple orders in the same delivery area and at the same delivery time, they are automatically merged into the same shipping batch, generating a combined order matching solution.

[0028] Specifically, the warehousing and logistics collaborative dynamic scheduling in step S5 includes:

[0029] Based on the outbound stacking position, corresponding loading and unloading equipment, outbound platform and vehicle arrival time in the matching scheme, the outbound hoisting operation sequence and platform loading sequence are scheduled according to the rule of combining first-come-first-served and priority-based, and storage operation instructions and vehicle arrival reservation instructions are generated.

[0030] By combining real-time traffic conditions and unloading time constraints at the receiving location, the optimal transportation route for vehicles is planned, and logistics transportation instructions are generated.

[0031] Real-time monitoring of abnormal events during execution, including inventory discrepancies, loading and unloading equipment malfunctions, vehicle delays, and abnormal road conditions. When an abnormal event affects order delivery, the rescheduling logic is automatically triggered to update the work sequence and transportation plan.

[0032] Specifically, step S6 includes:

[0033] At the four stages of outbound, loading, transportation, and receipt, the system collects node time, operator, vehicle information, and weight verification data by scanning digital identity tags, and uploads them to the traceability archive to achieve full-chain visualization of order execution.

[0034] Open the traceability query interface to the client, supporting the query of steel quality information and real-time logistics location by order number and steel batch number;

[0035] Periodically extract historical order matching accuracy, outbound efficiency, and on-time delivery rate data to iteratively optimize the weight parameters of the matching and scheduling model.

[0036] The beneficial effects of the steel warehousing logistics traceability and intelligent order matching and scheduling method proposed in this application are as follows:

[0037] 1. Construct a digital traceability system for the entire life cycle of steel, assign digital identities to each batch of steel and establish traceability files, break down data barriers between various links, and realize the linkage and traceability of quality, warehousing and logistics data. It can quickly locate the root cause of problems such as wrong goods, delivery deviations and quality disputes, while supporting customers to query traceability in real time, effectively improving supply chain transparency and fault tracing efficiency.

[0038] 2. Digital modeling of warehouse areas, stacking positions, loading and unloading equipment, and platforms is carried out to build a digital map of warehousing resources. Combined with real-time operation data, the inventory capacity, equipment and platform working status are dynamically updated to form a real-time dynamic inventory resource pool, effectively avoiding problems such as idle resources, misuse of stacking positions, and inventory deviations, and significantly improving the overall utilization rate of warehousing resources.

[0039] 3. Achieve collaborative scheduling and dynamic rescheduling across the entire warehousing and logistics chain. Combine order priority, inventory and logistics resources to plan the timing of outbound, loading and transportation operations, eliminating problems such as operational disconnect, congestion and resource waste. At the same time, it can identify emergencies such as equipment failure, vehicle delays and abnormal road conditions in real time, and automatically trigger rescheduling optimization schemes, significantly improving the stability of order delivery and the ability to adapt to different scenarios. Attached Figure Description

[0040] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0041] Figure 1 This is a flowchart of a steel warehousing and logistics traceability and intelligent order matching and scheduling method according to this application;

[0042] Figure 2 This is a flowchart illustrating the multi-objective intelligent matching process of the steel warehousing and logistics traceability and intelligent order matching and scheduling method proposed in this application.

[0043] Figure 3This is a flowchart illustrating the collaborative dynamic scheduling process of a steel warehousing and logistics traceability and intelligent order matching scheduling method proposed in this application. Detailed Implementation

[0044] To make the technical means, inventive features, objectives, and effects of this application easier to understand, the application is further described below with reference to specific illustrations. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0045] The present application will now be described in further detail with reference to the accompanying drawings.

[0046] like Figures 1-3 As shown in the figure, a method for steel warehousing and logistics traceability and intelligent order matching and scheduling according to an embodiment of this application includes the following steps:

[0047] S1. Collect basic attribute data, quality certificate data and storage location data of steel entering the warehouse, generate a unique digital identity for each batch of steel, and establish a traceability file for the entire life cycle of steel.

[0048] It should be noted that the basic attribute data of the steel includes parameters such as steel specifications, material grade, manufacturer, production batch, weight, length, width, thickness, production date, and qualification grade; the quality certificate data covers documents such as the steel mill's factory inspection report, third-party testing certificate, warehousing re-inspection record, and quality acceptance results; the storage location data includes storage location and operation data such as the steel's warehousing time, storage area number, stack code, stacking level, information on adjacent steel, and warehousing personnel. Based on the collected full-dimensional data, an encrypted hash algorithm is used to assign a digital identity to each batch and each piece of steel entering the warehouse. This identity is used throughout the entire process of steel storage, outbound, transportation, and delivery.

[0049] At the same time, a cloud-based traceability archive database is built, which links various collected data, subsequent operation data, logistics data, and receipt data to corresponding digital identity identifiers in real time, constructing a full life cycle traceability archive for steel from production and warehousing to final receipt, so as to realize that steel information can be queried, source can be traced, quality can be verified, and responsibility can be defined.

[0050] S2. Digitally model the storage area, stacking positions, loading and unloading equipment, and platform resources, and update the inventory quantity, status, and occupancy of steel in each stacking position in real time to form a dynamic inventory resource pool.

[0051] It should be noted that the storage area is modeled in a grid-like manner, dividing it into functional zones such as ambient temperature zone, moisture-proof zone, and heavy-load zone, and recording the spatial parameters, load limits, and environmental parameters of the storage area; each stack is modeled in detail, recording basic information such as stack space dimensions, maximum stacking load capacity, stacking specification compatibility, and occupancy status; loading and unloading equipment is modeled, recording equipment model, operating radius, load limit, operating efficiency, equipment status, and maintenance information; and platform resources are modeled, statistically analyzing parameters such as the number of platforms, operating channels, vehicle parking capacity, loading and unloading compatibility type, and idle time periods.

[0052] After modeling is completed, the system connects in real time with warehouse monitoring, equipment sensors, and manual data entry terminals to dynamically update the real-time inventory quantity, stacking status, occupancy time, and pending shipment status of steel at each stack. It also simultaneously updates the occupancy, idleness, fault, and maintenance status information of various warehouse resources in real time, and integrates all effective inventory resources and warehouse operation resources in real time to form a dynamic inventory resource pool that can be called in real time, dynamically updated, and with accurate data.

[0053] S3. Receive customer orders and perform structured parsing to extract steel demand parameters, delivery requirements, and logistics parameters from the orders. Calculate order priority based on multi-dimensional evaluation indicators.

[0054] It should be noted that, based on the parsed standardized order data, a multi-dimensional order priority evaluation index system is built. The index includes core dimensions such as customer level, order urgency, risk of delivery delay, order amount, purchase volume, customer cooperation priority, and special customization requirement level. Through weighted scoring algorithms and analytic hierarchy process, each index is quantitatively calculated to accurately determine the priority level of each order, distinguish the execution order of expedited orders, regular orders, batch orders, and pre-sale orders, and provide core sorting basis for subsequent order scheduling and matching, ensuring that high-priority orders are fulfilled first.

[0055] S4. Based on order priority, with the optimization goals of highest inventory matching degree, lowest outbound operation cost and best logistics transportation efficiency, select suitable inventory stacking locations from the dynamic inventory resource pool, match the corresponding loading and unloading resources and logistics capacity, and generate an initial order matching scheme.

[0056] It should be noted that inventory matching prioritizes the compatibility of the steel specifications, quantity, and quality grade of the stacking locations with the order requirements, and prioritizes matching stacking resources that do not require unstacking or restacking and have sufficient inventory; outbound operation costs are comprehensively calculated based on parameters such as stacking location material retrieval distance, equipment energy consumption, labor costs, and stacking location adjustment costs; logistics and transportation efficiency mainly refers to factors such as transportation route distance, capacity matching, road conditions, and transportation timeliness.

[0057] Based on the constructed dynamic inventory resource pool, intelligent algorithms are used to traverse and filter all suitable inventory resources. Combined with the operational capabilities of warehousing equipment and the logistics capacity resource ledger, the system completes the precise matching of inventory stacking locations, loading and unloading equipment, and logistics vehicle capacity, avoiding problems such as mismatch, omission, and inefficient matching. It generates an initial order matching scheme that takes into account order priority, matching accuracy, operational economy, and transportation efficiency, thereby achieving optimal resource allocation.

[0058] S5. Based on the initial order matching scheme, combined with the time constraints of warehousing operations and logistics capacity, generate the outbound operation sequence, loading operation plan and vehicle transportation route, and send them to the warehousing execution end and the logistics execution end. During the execution process, collect abnormal information in real time and trigger dynamic rescheduling.

[0059] It should be noted that warehousing operation timing constraints include avoiding conflicts in stacking operations, queuing equipment operation times, prioritizing multiple orders, and warehouse access rules; logistics capacity timing constraints include vehicle reservation times, transportation scheduling order, multi-site delivery times, and capacity schedule conflicts. Based on these various timing constraints, the system automatically generates standardized outbound operation sequences, refined loading operation plans, and optimal vehicle transportation routes: clearly defining the outbound time, picking stacking location, operating equipment, personnel, and operation duration for each batch of steel; formulating plans for batch loading sequence, loading quantity, and loading duration; and planning the optimal transportation route based on real-time road conditions, delivery points, and capacity status.

[0060] Subsequently, all work plans and scheduling instructions are sent in real time to the warehouse execution terminals, equipment control terminals, and logistics scheduling terminals to guide standardized on-site operations. Simultaneously, throughout the entire process, various anomalies are collected in real time, including equipment malfunctions, inventory discrepancies, vehicle delays, weather impacts, customer changes in requirements, and on-site congestion. Once an anomaly triggers a scheduling deviation threshold, the system automatically activates a dynamic rescheduling mechanism, adjusting pallet matching, equipment scheduling, transportation routes, and work sequences in real time to ensure continuous and efficient order processing.

[0061] S6. Collect data on the entire order execution chain, including outbound, loading, in transit, and receipt nodes, and update the traceability files of the corresponding batch of steel. At the same time, feed the execution result data back to the matching and scheduling model to complete the model iteration and optimization.

[0062] It should be noted that, on the one hand, all execution data is synchronized and linked in real time to the full lifecycle traceability file of the corresponding batch of steel, improving the data of the outbound, logistics, and delivery stages of the file, realizing a complete closed loop of traceability information for the entire steel process, and ensuring that the traceability data is fully covered, traceable, and verifiable. On the other hand, order matching results, scheduling plans, on-site execution data, abnormal data, and fulfillment result data are all fed back to the intelligent order matching and scheduling model to form a data closed loop.

[0063] Through big data analysis and machine learning algorithms, the system continuously identifies model matching biases, inefficient scheduling scenarios, and frequent anomalies. It iteratively optimizes priority evaluation weights, multi-objective matching algorithms, time-series scheduling rules, and anomaly response strategies to continuously improve order matching accuracy, warehousing operation efficiency, and logistics scheduling rationality. This enables the system model to be self-adaptive and self-optimizing, thereby continuously enhancing the intelligence level and fulfillment capabilities of the overall warehousing and logistics scheduling system.

[0064] In one embodiment of this application, step S2 specifically includes:

[0065] Based on the functional zoning of the storage area, the load-bearing limit of the stack, the suitable storage categories, and the rated load of the corresponding loading and unloading equipment, a digital map of storage resources is constructed, marking the types of steel that can be stored, the maximum storage capacity, and the corresponding loading and unloading equipment number for each stack.

[0066] Based on real-time data from inbound, transfer, and outbound operations, the system dynamically updates the steel inventory quantity, occupancy status, and available storage capacity at each storage location. It also synchronously updates the busy / idle status and available time periods of loading / unloading equipment and outbound platforms, forming a real-time dynamic inventory resource pool.

[0067] It should be noted that the system combines four types of parameters—functional zoning of the storage area, maximum load-bearing capacity of each stack, compatible storage categories of steel, and rated load of loading and unloading equipment—to build a digital map of storage resources. Specifically, the storage area is divided into functional zones such as raw material area, finished product area, processing area, return storage area, and special material protection area according to business attributes. Based on measured load-bearing capacity, stacks are categorized into heavy-duty, light-duty, and precision-specific types. Furthermore, suitable profiles, plates, pipes, and bars are matched according to the storage characteristics of the steel, mitigating storage risks such as misplacement, overloading, and space mismatch.

[0068] The system assigns a unique code to each storage location within the warehouse, labeling it with the type of steel it can store, its maximum storage weight and volume, and binding it to the corresponding loading and unloading equipment numbers such as overhead cranes and forklifts. This enables digital archiving and visual management of warehouse physical resources. Simultaneously, the system collaborates in real-time with inbound, transfer, and outbound operation terminals, sensing devices, and the WMS system to collect on-site operational data and support dynamic updates to the status of warehouse resources.

[0069] The dynamic updates include real-time inventory quantities, occupied / idle / locked status, and remaining available storage capacity for each pallet location. Simultaneously, the busy / idle status of loading and unloading equipment, maintenance details, and the occupied and available time slots for outbound platforms are updated. Through real-time iteration of data across the entire domain, a dynamic inventory resource pool with accurate data and real-time status coverage is formed, providing reliable data support for subsequent order matching, resource scheduling, and work scheduling.

[0070] In one embodiment of this application, in step S3, the steel demand parameters of the order include category, material, specifications, quantity, and processing requirements; delivery requirements include delivery time, delivery location, and acceptance standards; and logistics parameters include transportation mode, vehicle requirements, and unloading conditions.

[0071] The multi-dimensional evaluation indicators include order delivery urgency, customer level, order amount, and logistics fulfillment difficulty. The weight of each indicator is determined by the analytic hierarchy process, and the order priority value is calculated. The higher the priority value, the higher the scheduling ranking.

[0072] It should be noted that the delivery time clearly specifies the latest delivery node, batch cycle, and late delivery constraints; the delivery location precisely identifies the receiving site and distinguishes between same-city and cross-regional delivery; acceptance standards include verification of appearance, dimensional tolerances, material performance, and warranty documents. Logistics parameters are used to standardize transportation performance conditions, including transportation mode, vehicle requirements, and unloading conditions: transportation modes cover full truckload, less-than-truckload (LTL), dedicated truck, and trunk logistics.

[0073] Order delivery urgency, customer level, order amount, and logistics fulfillment difficulty are selected as multi-dimensional evaluation indicators to achieve quantitative grading of order priorities. To reduce the subjectivity of manual evaluation, the Analytic Hierarchy Process (AHP) is adopted. By constructing a hierarchical model, pairwise comparison matrices, and consistency checks, the weights of each indicator are scientifically determined, and the weighted calculation yields the order priority value. The higher the priority value, the earlier the order is scheduled, and the more priority it can occupy in terms of warehousing and logistics resources, ensuring priority fulfillment for high-urgency, high-value orders.

[0074] In one embodiment of this application, the order priority value P is calculated using the following formula:

[0075] P = W1·E + W2·C + W3·A + W4·L

[0076] Where E is the order delivery urgency score, C is the customer level score, A is the order amount score, L is the logistics fulfillment difficulty score, and W1, W2, W3, and W4 are the corresponding weights, which are determined by the analytic hierarchy process and satisfy W1+W2+W3+W4=1.

[0077] It should be noted that this model can comprehensively reflect the combined impact of order timeliness, customer value, order revenue and logistics difficulty, adapt to complex scheduling scenarios in steel warehousing and logistics, and achieve standardized and quantifiable output of order priorities.

[0078] Among them, E is the order delivery urgency score, which is comprehensively evaluated based on the remaining delivery time, expedited attributes, impact of overdue orders, and customer's urging status; C is the customer level score, which is assigned a grade based on the customer's cooperation level, annual transaction volume, reputation, and performance record; A is the order amount score, which is quantitatively scored by combining the order transaction volume and profit level; L is the logistics fulfillment difficulty score, which is comprehensively evaluated based on transportation distance, road conditions, unloading conditions, weather, and the scarcity of transportation capacity. The better the fulfillment conditions, the higher the corresponding score.

[0079] W1, W2, W3, and W4 are the weight coefficients corresponding to the four evaluation indicators. They were obtained through iterative optimization using the analytic hierarchy process combined with industry scenarios, scheduling strategies, and historical data. Their rationality was ensured through consistency verification. The weight configuration is in line with actual business needs and can take into account delivery timeliness, customer value, corporate revenue, and contract fulfillment feasibility.

[0080] In one embodiment of this application, step S4, multi-target intelligent matching, includes:

[0081] Based on the steel category, material, specifications, and quantity requirements of the order, a set of candidate inventory locations with completely matching attributes is selected from the dynamic inventory resource pool;

[0082] For each candidate inventory location, calculate the outbound operation cost, logistics transportation distance, and estimated transportation time.

[0083] A multi-objective optimization function is constructed, with objective functions including maximizing inventory matching accuracy, minimizing overall operating costs, and minimizing order response time. Constraints include total inventory meeting order requirements, load matching of loading and unloading equipment, and matching of vehicle load and volume. An improved genetic algorithm is used to solve for the optimal matching scheme.

[0084] For orders that cannot be fulfilled by a single pallet position, they are automatically split into multiple suitable pallet positions. For multiple orders in the same delivery area and at the same delivery time, they are automatically merged into the same shipping batch, generating a combined order matching solution.

[0085] It should be noted that the system first traverses the real-time data of the dynamic inventory resource pool based on the steel category, material, specifications, and quantity requirements of the order, accurately matches the stacking location attributes, eliminates invalid stacking locations with inconsistent parameters or insufficient inventory, and filters out a set of candidate stacking locations that meet the basic fulfillment conditions, thus ensuring the accuracy of inventory matching from the source.

[0086] For each candidate pallet location, the system automatically calculates the overall outbound operation cost, transportation distance, and estimated transportation time. The outbound operation cost includes hoisting energy consumption, equipment wear and tear, labor costs, and warehouse transfer costs; the transportation distance and time are comprehensively estimated based on road network conditions, speed limits, and loading / unloading waiting times, providing a quantitative basis for subsequent multi-objective optimization.

[0087] The system constructs a multi-objective optimization function with the core optimization objectives of maximizing inventory matching accuracy, minimizing overall operating costs, and minimizing order response time. Simultaneously, multiple constraints are set to ensure that the total inventory meets order demand, loading and unloading equipment loads are adapted, and vehicle load and volume are compliant. This solution employs an improved genetic algorithm, optimizing the population iteration and mutation mechanism to overcome the problems of local optima and slow convergence in traditional algorithms, and quickly solves for the optimal resource matching scheme.

[0088] For large orders with insufficient inventory at a single pallet, the system can intelligently split the order and allocate it to multiple suitable pallet locations based on the remaining capacity, operating costs, and location advantages of each pallet, enabling coordinated outbound processing across multiple pallet locations and ensuring full order delivery. For multiple orders with consistent delivery areas, overlapping delivery times, and no transportation conflicts, the system supports automatic order consolidation and unified planning of transport capacity and routes to form a streamlined transportation solution, effectively reducing overall logistics costs and improving scheduling efficiency.

[0089] In one embodiment of this application, the dynamic scheduling of warehouse logistics coordination in step S5 includes:

[0090] Based on the outbound stacking position, corresponding loading and unloading equipment, outbound platform and vehicle arrival time in the matching scheme, the outbound hoisting operation sequence and platform loading sequence are scheduled according to the rule of combining first-come-first-served and priority-based, and storage operation instructions and vehicle arrival reservation instructions are generated.

[0091] By combining real-time traffic conditions and unloading time constraints at the receiving location, the optimal transportation route for vehicles is planned, and logistics transportation instructions are generated.

[0092] Real-time monitoring of abnormal events during execution, including inventory discrepancies, loading and unloading equipment malfunctions, vehicle delays, and abnormal road conditions. When an abnormal event affects order delivery, the rescheduling logic is automatically triggered to update the work sequence and transportation plan.

[0093] It should be noted that the system automatically schedules the hoisting operations and loading times at the platform, generates warehousing operation instructions and vehicle arrival reservation instructions, and sends them to the on-site terminals and driver terminals, achieving seamless connection between vehicle arrival and warehouse operations, reducing waiting time and resource idleness.

[0094] By simultaneously combining real-time road conditions, traffic control, weather conditions, and the recipient's unloading time constraints, the system intelligently plans transportation routes with the goals of minimizing time, optimizing costs, and ensuring smooth driving. It generates logistics transportation instructions that include routes, estimated durations, and stop reminders, guiding vehicles to efficiently fulfill their transportation obligations.

[0095] The system monitors for anomalies throughout the fulfillment process, including inventory discrepancies, loading and unloading equipment malfunctions, vehicle delays, sudden road condition changes, and alterations to unloading times. When an anomaly affects delivery schedules, the system automatically triggers rescheduling logic to quickly re-match pallet locations, equipment, platforms, and transport capacity, updating work sequences and transportation plans in real time to reduce anomaly risks and ensure stable and reliable warehousing and logistics fulfillment.

[0096] In one embodiment of this application, step S6 specifically includes:

[0097] At the four stages of outbound, loading, transportation, and receipt, the system collects node time, operator, vehicle information, and weight verification data by scanning digital identity tags, and uploads them to the traceability archive to achieve full-chain visualization of order execution.

[0098] Open the traceability query interface to the client, supporting the query of steel quality information and real-time logistics location by order number and steel batch number;

[0099] Periodically extract historical order matching accuracy, outbound efficiency, and on-time delivery rate data to iteratively optimize the weight parameters of the matching and scheduling model.

[0100] It should be noted that the operators scan the code on the terminal to automatically collect node time, operator and vehicle information, weight verification and operation video data, and upload them to the traceability archive in real time with encryption, so as to achieve full traceability, accountability and ensure delivery quality and operation compliance.

[0101] The system provides a traceability query interface to clients, supporting access from multiple terminals. Users can query steel quality data and batch information by order number and steel batch number, and can also view logistics location, transportation progress and estimated delivery time in real time, improving order fulfillment transparency and customer experience.

[0102] The system regularly compiles key metrics such as historical order matching accuracy, outbound efficiency, and on-time delivery rate. Through big data analysis, it identifies model adaptation shortcomings and iteratively optimizes order priority weights, matching algorithm parameters, and scheduling timing rules to continuously improve the model's scenario adaptability, matching accuracy, and scheduling efficiency, thereby achieving long-term optimization and upgrading of the intelligent scheduling system.

[0103] The present application and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present application. The actual structure is not limited to this. In conclusion, if a person skilled in the art is inspired by this description and designs a similar structure and embodiment without departing from the spirit of the present application, such design should fall within the protection scope of the present application.

Claims

1. A method for steel warehousing and logistics traceability and intelligent order matching and scheduling, characterized in that, Includes the following steps: S1. Collect basic attribute data, quality certificate data and storage location data of steel entering the warehouse, generate a unique digital identity for each batch of steel, and establish a traceability file for the entire life cycle of steel. S2. Digitally model the storage area, stacking positions, loading and unloading equipment, and platform resources, and update the inventory quantity, status, and occupancy of steel in each stacking position in real time to form a dynamic inventory resource pool. S3. Receive customer orders and perform structured parsing to extract steel demand parameters, delivery requirements and logistics parameters of the orders, and calculate order priority based on multi-dimensional evaluation indicators; S4. Based on order priority, with the optimization goals of highest inventory matching degree, lowest outbound operation cost and best logistics transportation efficiency, select suitable inventory stacking locations from the dynamic inventory resource pool, match the corresponding loading and unloading resources and logistics capacity, and generate an initial order matching scheme. S5. Based on the initial order matching scheme, combined with the time constraints of warehousing operations and logistics capacity, generate outbound operation sequence, loading operation plan and vehicle transportation route, and send them to the warehousing execution end and logistics execution end. During the execution process, collect abnormal information in real time and trigger dynamic rescheduling. S6. Collect data on the entire order execution chain, including outbound, loading, in transit, and receipt nodes, and update the traceability files of the corresponding batch of steel. At the same time, feed the execution result data back to the matching and scheduling model to complete the model iteration and optimization.

2. The method for steel warehousing and logistics traceability and intelligent order matching and scheduling according to claim 1, characterized in that, Step S2 specifically includes: Based on the functional zoning of the storage area, the load-bearing limit of the stack, the suitable storage categories, and the rated load of the corresponding loading and unloading equipment, a digital map of storage resources is constructed, marking the types of steel that can be stored, the maximum storage capacity, and the corresponding loading and unloading equipment number for each stack. Based on real-time data from inbound, transfer, and outbound operations, the system dynamically updates the steel inventory quantity, occupancy status, and available storage capacity at each storage location. It also synchronously updates the busy / idle status and available time periods of loading / unloading equipment and outbound platforms, forming a real-time dynamic inventory resource pool.

3. The method for traceability and intelligent order matching and scheduling of steel warehousing and logistics according to claim 1, characterized in that, In step S3, the steel demand parameters for the order include category, material, specifications, quantity, and processing requirements; delivery requirements include delivery time, delivery location, and acceptance standards; and logistics parameters include transportation method, vehicle requirements, and unloading conditions. The multi-dimensional evaluation indicators include order delivery urgency, customer level, order amount, and logistics fulfillment difficulty. The weight of each indicator is determined by the analytic hierarchy process (AHP), and the order priority value is calculated. The higher the priority value, the higher the scheduling ranking.

4. The method for steel warehousing and logistics traceability and intelligent order matching and scheduling according to claim 1, characterized in that, The order priority value P is calculated using the following formula: P = W1·E + W2·C + W3·A + W4·L Where E is the order delivery urgency score, C is the customer level score, A is the order amount score, L is the logistics fulfillment difficulty score, and W1, W2, W3, and W4 are the corresponding weights, which are determined by the analytic hierarchy process and satisfy W1+W2+W3+W4=1.

5. The method for steel warehousing and logistics traceability and intelligent order matching and scheduling according to claim 1, characterized in that, The multi-target intelligent matching in step S4 includes: Based on the steel category, material, specifications, and quantity requirements of the order, a set of candidate inventory locations with completely matching attributes is selected from the dynamic inventory resource pool; For each candidate inventory location, calculate the outbound operation cost, logistics transportation distance, and estimated transportation time. A multi-objective optimization function is constructed, with objective functions including maximizing inventory matching accuracy, minimizing overall operating costs, and minimizing order response time. Constraints include total inventory meeting order requirements, load matching of loading and unloading equipment, and matching of vehicle load and volume. An improved genetic algorithm is used to solve for the optimal matching scheme. For orders that cannot be fulfilled by a single pallet position, they are automatically split into multiple suitable pallet positions. For multiple orders in the same delivery area and at the same delivery time, they are automatically merged into the same shipping batch, generating a combined order matching solution.

6. The method for steel warehousing and logistics traceability and intelligent order matching and scheduling according to claim 1, characterized in that, The warehousing and logistics collaborative dynamic scheduling in step S5 includes: Based on the outbound stacking position, corresponding loading and unloading equipment, outbound platform and vehicle arrival time in the matching scheme, the outbound hoisting operation sequence and platform loading sequence are scheduled according to the rule of combining first-come-first-served and priority-based, and storage operation instructions and vehicle arrival reservation instructions are generated. By combining real-time traffic conditions and unloading time constraints at the receiving location, the optimal transportation route for vehicles is planned, and logistics transportation instructions are generated. Real-time monitoring of abnormal events during execution, including inventory discrepancies, loading and unloading equipment malfunctions, vehicle delays, and abnormal road conditions. When an abnormal event affects order delivery, the rescheduling logic is automatically triggered to update the work sequence and transportation plan.

7. The method for steel warehousing and logistics traceability and intelligent order matching and scheduling according to claim 1, characterized in that, Step S6 specifically includes: At the four stages of outbound, loading, transportation, and receipt, the system collects node time, operator, vehicle information, and weight verification data by scanning digital identity tags, and uploads them to the traceability archive to achieve full-chain visualization of order execution. Open the traceability query interface to the client, supporting the query of steel quality information and real-time logistics location by order number and steel batch number; Periodically extract historical order matching accuracy, outbound efficiency, and on-time delivery rate data to iteratively optimize the weight parameters of the matching and scheduling model.

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