A logistics resource collaborative optimization configuration method for warehouse and distribution integration

Abstract

This invention relates to the field of warehousing and distribution collaborative optimization technology, specifically to a method for collaborative optimization and allocation of logistics resources for integrated warehousing and distribution. The method includes collecting last-mile delivery timeliness deviations and order delay status, generating a reverse traction signal, coupling and matching the reverse traction signal with the order delay status to generate a resource pre-configuration scheme; performing outbound operation pre-configuration on the warehousing side and capacity pre-configuration on the delivery side before the order arrives at the warehousing node to obtain the pre-configured warehousing and distribution collaborative state; monitoring actual execution deviations in real time, dynamically correcting the resource pre-configuration scheme, and redeploying it to the warehousing and delivery nodes, causing the warehousing node to re-execute the outbound operation pre-configuration and the delivery node to re-execute the capacity pre-configuration, forming a closed-loop iterative optimization. This invention forms a continuously adaptive closed-loop optimization process for warehousing and distribution collaboration, improving the overall utilization efficiency of logistics resources and the reliability of delivery in complex logistics networks.

Description

Technical Field

[0001] This invention relates to the field of warehousing and distribution collaborative optimization technology, and in particular to a method for collaborative optimization of logistics resources for integrated warehousing and distribution. Background Technology

[0002] With the rapid development of instant retail, e-commerce logistics, and regional warehousing and distribution networks, logistics systems are placing higher demands on order fulfillment timeliness, warehousing efficiency, and the ability to coordinate delivery resources. Existing logistics systems typically employ a scheduling approach where warehousing and delivery are independent of each other. Warehouse nodes perform picking, packing, and outbound operations based on the current order status, while delivery nodes schedule vehicles, plan routes, and arrange last-mile delivery based on the outbound order results. This approach is essentially a passive response model where scheduling occurs only after the order arrives at the warehouse. There is a lack of proactive coordination between warehousing and delivery resource allocation, making it difficult to adapt to the dynamic fulfillment needs of high-frequency order fluctuations and complex logistics network environments.

[0003] In existing technologies, warehousing nodes typically schedule outbound shipments based solely on order queue lengths or fixed priority rules, while delivery nodes usually allocate capacity based on vehicle availability or regional delivery plans. There is a lack of collaborative feedback mechanisms between the warehousing and delivery sides based on fulfillment pressure. When last-mile delivery nodes experience fulfillment delays, insufficient capacity, or delivery congestion, the relevant fulfillment pressure cannot be promptly transmitted to upstream warehousing nodes. This causes warehousing nodes to continue outbound operations at their original pace, easily leading to problems such as vehicles waiting to load, order backlogs, and last-mile fulfillment delays. Traditional warehousing and distribution collaboration methods mostly rely on static resource allocation schemes, lacking dynamic correction capabilities based on real-time deviation feedback during actual execution. When warehousing node outbound delays, abnormal delivery vehicle arrivals, or fluctuations in last-mile fulfillment timeliness occur, the system struggles to promptly adjust warehousing and delivery resources, reducing overall logistics resource utilization efficiency and order fulfillment stability. Summary of the Invention

[0004] This invention provides a method for the collaborative optimization of logistics resources for integrated warehousing and distribution. It can reverse the pressure of last-mile delivery, realize the forward collaborative configuration of warehousing and distribution resources, and continuously perform dynamic correction and closed-loop optimization based on actual execution deviations.

[0005] A method for collaborative optimization of logistics resource allocation for integrated warehousing and distribution includes the following steps:

[0006] S1: Collect the last-mile delivery time deviation of the delivery node and the order retention status of the warehousing node, generate a reverse traction signal based on the last-mile delivery time deviation, and couple and match the reverse traction signal with the order retention status to generate a resource pre-configuration scheme.

[0007] S2: According to the resource pre-configuration scheme, before the order arrives at the warehousing node, perform the outbound operation pre-configuration operation on the warehousing side and the transportation capacity pre-configuration operation on the delivery side to obtain the pre-configured warehousing and distribution coordination status.

[0008] S3: Monitor the actual execution deviation of the pre-configured warehouse and distribution coordination status in real time during the execution process, dynamically correct the resource pre-configuration scheme based on the actual execution deviation, and redeploy the corrected pre-configuration scheme to the warehouse node and the distribution node, so that the warehouse node re-executes the outbound operation pre-configuration operation and the distribution node re-executes the transportation capacity pre-configuration operation, forming a closed-loop iterative optimization.

[0009] Optionally, S1 specifically includes:

[0010] S11: Calculate the end-of-line fulfillment timeliness deviation by the difference between the end-of-line fulfillment timestamp of the delivery node and the preset promised timeliness, and calculate the order retention status by weighting the order queue length and retention time of the warehousing node;

[0011] S12: Map the last-mile delivery timeliness deviation to a time pressure coefficient, and perform attenuation correction based on the network distance between the delivery node and the warehousing node to generate the reverse traction signal;

[0012] S13: The reverse traction signal is used as the forward demand weight and the order stagnation status is used as the reverse constraint factor. The two are input into a preset resource matching function for weighted coupling, and the resource pre-configuration scheme is output. The resource pre-configuration scheme includes the pre-outbound task list of the warehousing node and the pre-scheduled transportation capacity list of the delivery node.

[0013] Optionally, S12 specifically includes:

[0014] Obtain the overall end-of-line delivery timeliness deviation index and the preset maximum timeliness deviation benchmark value of the delivery node, and calculate the time pressure coefficient based on the proportional relationship between the overall end-of-line delivery timeliness deviation index and the preset maximum timeliness deviation benchmark value;

[0015] Obtain transportation route data corresponding to delivery nodes and warehousing nodes, and calculate network distance based on the cumulative travel distance in the actual transportation path between warehousing nodes and delivery nodes;

[0016] The time pressure coefficient is gradually corrected based on the network distance to obtain the distance-corrected pressure value; as the network distance increases, the pressure intensity corresponding to the time pressure coefficient decreases.

[0017] When the network distance decreases, the pressure intensity corresponding to the time pressure coefficient is increased; a reverse traction signal is generated based on the pressure value after distance correction, and the reverse traction signal is used as the forward demand weight input in the subsequent resource matching function;

[0018] Obtain the current order queue length and average order dwell time of the warehouse node, and calculate the order dwell status by weighting the order queue length and average order dwell time.

[0019] The overall end-point delivery timeliness deviation index of the delivery node and the order retention status are used as data inputs for the generation of subsequent reverse traction signals.

[0020] Optionally, the resource matching function specifically includes:

[0021] Acquire reverse traction signals and order standby status, and perform unified dimension processing on reverse traction signals and order standby status;

[0022] The processed reverse traction signal is used as the forward demand weight on the resource demand side, and the processed order stagnation status is used as the reverse constraint factor on the resource supply side. They are input together into the resource matching function for coupled calculation to obtain the resource matching result value.

[0023] The priority of warehousing resource demand and transportation resource demand corresponding to the delivery node are determined based on the resource matching result value.

[0024] When the resource matching result value exceeds the preset resource trigger threshold, increase the number of pre-outbound tasks at the warehouse node and the number of pre-scheduled transportation capacity at the delivery node; when the resource matching result value is lower than the preset resource trigger threshold, reduce the resource pre-configuration intensity or maintain the current resource configuration status.

[0025] Based on the resource matching results, generate a pre-outbound task list for the warehousing node and a pre-scheduled transportation capacity list for the delivery node, and output the pre-outbound task list and the pre-scheduled transportation capacity list together as a resource pre-configuration scheme to the corresponding warehousing node and delivery node.

[0026] Optionally, S2 specifically includes:

[0027] S21, parse the pre-outbound task list and pre-scheduled transport capacity list in the resource pre-configuration scheme;

[0028] S22, Before the order arrives at the warehousing node, perform the warehousing-side outbound operation pre-configuration operation in advance according to the pre-outbound task list;

[0029] S23, Based on the pre-scheduled capacity list, perform the pre-configuration operation of the delivery-side capacity in advance;

[0030] S24, perform spatiotemporal correlation alignment between the pre-outbound readiness state and the pre-transportation readiness state to obtain the pre-configured warehousing and distribution coordination state, which includes the coupling relationship between the expected outbound time and the expected shipment time of the order.

[0031] Optionally, the pre-configuration operation for warehouse-side outbound operations specifically includes pre-reserving inventory buffer space, pre-generating picking waves and assigning pickers, pre-printing outbound waybills and pre-binding outbound platforms to form a pre-outbound ready state.

[0032] Optionally, the delivery-side capacity pre-configuration operation specifically includes pre-locking delivery vehicles and driver time slots, pre-generating delivery routes and time windows, and pre-allocating terminal stations or express locker resources to form a pre-capacity ready state.

[0033] Optionally, S3 specifically includes:

[0034] S31: During the execution of the pre-configured warehouse and distribution coordination state, the actual execution deviation is collected in real time at a preset sampling period;

[0035] S32: When any of the actual execution deviations exceeds the preset deviation tolerance threshold, a dynamic correction of the current resource pre-configuration scheme is triggered;

[0036] S33: Redeploy the revised pre-configuration scheme to the warehousing node and the delivery node, so that the warehousing node re-executes the outbound operation pre-configuration operation according to the revised pre-outbound task list, and the delivery node re-executes the capacity pre-configuration operation according to the revised pre-scheduled capacity list;

[0037] S34: Repeat the above steps of monitoring, triggering, correcting, redeploying, and re-executing until all actual execution deviations continuously converge within the deviation tolerance threshold, forming a closed-loop iterative optimization.

[0038] Optionally, the actual execution deviations collected specifically include the outbound timing deviation between the actual outbound time and the expected outbound time of the warehousing node, the capacity positioning deviation between the actual vehicle arrival time and the expected arrival time of the delivery node, and the terminal secondary deviation between the actual fulfillment time and the preset promised time of the terminal node.

[0039] Optionally, the dynamic correction specifically includes taking the actual execution deviation as feedback input, combining it with the real-time changes in the current order retention status, recalculating the reverse traction signal through a deviation compensation algorithm, and performing secondary coupling matching based on the recalculated reverse traction signal and the current order retention status to generate a corrected resource pre-configuration scheme.

[0040] The beneficial effects of this invention are:

[0041] This invention constructs a warehousing and distribution linkage mechanism based on last-mile delivery timeliness deviation, a reverse traction signal, and a resource pre-configuration scheme. This transforms the traditional passive response approach, which relies on order arrival at the warehouse for resource scheduling, into a proactive predictive approach that pre-drives the coordinated allocation of warehousing and distribution resources based on last-mile delivery pressure. By mapping the last-mile delivery timeliness deviation of delivery nodes to a time pressure coefficient and attenuating it by combining the network distance between delivery and warehousing nodes, the delivery pressure can be dynamically transmitted upstream to warehousing nodes along the warehousing and distribution network. Furthermore, by combining this with order retention status for weighted coupling matching, a pre-outbound task list for warehousing nodes and a pre-scheduled capacity list for delivery nodes are generated in advance. This achieves pre-coordinated linkage between warehousing and distribution resources, reducing resource congestion, vehicle waiting, and outbound delays caused by orders concentrating at the warehouse, and improving the overall delivery timeliness stability of the warehousing and distribution system.

[0042] This invention performs pre-configuration operations on both the warehousing and distribution sides before orders arrive at the warehousing node, including pre-reservation of inventory buffer space, pre-generation of picking waves, pre-assignment of pickers, pre-binding of outbound platforms, pre-locking of delivery vehicles, pre-generation of delivery routes, and pre-allocation of last-mile delivery resources. Furthermore, it aligns the pre-outbound readiness state with the pre-capacity readiness state in a spatiotemporal manner, creating a dynamic coupling relationship between the expected outbound time of orders and the expected departure time of delivery vehicles. Compared to the traditional independent scheduling method between the warehousing and distribution sides, this invention establishes a coordinated correspondence between the warehousing outbound rhythm and the delivery rhythm, reducing problems such as waiting for vehicles at warehousing nodes, waiting for loading on vehicles, and idle last-mile resources. This improves the efficiency of collaborative operations between warehousing and distribution nodes and reduces the risk of time mismatch and resource idleness during warehousing and distribution coordination.

[0043] This invention constructs a closed-loop iterative optimization mechanism based on feedback from actual execution deviations. During the collaborative execution of warehousing and distribution, it collects outbound timing deviations, capacity positioning deviations, and last-mile secondary deviations in real time. When the actual execution deviation exceeds the deviation tolerance threshold, it dynamically compensates and corrects the reverse traction signal based on real-time changes in order delay status, and then re-executes resource matching, resource deployment, and warehousing and distribution coordination adjustments. This allows warehousing and distribution nodes to continuously reconfigure and dynamically optimize resources based on real-time fulfillment status. Compared to traditional static scheduling methods, this invention improves the real-time adaptability of the warehousing and distribution system to order fluctuations, capacity changes, and fulfillment anomalies, gradually converging various actual execution deviations within the deviation tolerance range. This forms a continuously adaptive closed-loop optimization process for warehousing and distribution collaboration, improving the overall utilization efficiency of logistics resources and the fulfillment reliability in complex logistics networks. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this invention 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 for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of closed-loop correction according to an embodiment of the present invention. Detailed Implementation

[0047] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. For some well-known technologies, those skilled in the art may also use other alternative methods to implement the invention. Moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0048] like Figures 1-2 As shown, a method for collaborative optimization of logistics resources for integrated warehousing and distribution includes the following steps:

[0049] S1: Collect the last-mile delivery time deviation of the delivery node and the order retention status of the warehousing node, generate a reverse traction signal based on the last-mile delivery time deviation, and couple and match the reverse traction signal with the order retention status to generate a resource pre-configuration scheme.

[0050] S1 specifically includes:

[0051] S11 calculates the end-of-line fulfillment timeliness deviation by the difference between the end-of-line fulfillment timestamp of the delivery node and the preset promised timeliness, and calculates the order retention status by weighting the order queue length and retention time of the warehousing node.

[0052] S111, Obtain the actual fulfillment completion timestamp of each order in the delivery node. and the pre-set commitment fulfillment timestamp for the corresponding order. The time difference between the two is used to calculate the single-level end-of-line performance deviation, which is expressed as: ;

[0053] in, This indicates a deviation in the timeliness of final delivery. Indicates the timestamp of the actual completion of order fulfillment. This indicates the pre-set commitment fulfillment timestamp for the order.

[0054] when When, it indicates that the order has experienced a delay in fulfillment; when When this occurs, it indicates that the order has been fulfilled ahead of schedule.

[0055] S112 aggregates the delivery timeliness deviations of multiple orders at the delivery node within a preset statistical period to obtain the overall delivery timeliness deviation index, expressed as:

[0056] ;

[0057] S112 first collects order fulfillment data from delivery nodes in segments according to preset statistical periods of 5 minutes, 15 minutes, 30 minutes, or 1 hour. Within each statistical period, the system reads the actual fulfillment timestamps and preset promised fulfillment timestamps for all completed orders under that delivery node, and calculates the end-of-line fulfillment timeliness deviation for each order. After calculating the timeliness deviation for a single order, a timeliness deviation data set corresponding to the current statistical period is established, and all end-of-line fulfillment timeliness deviations in the data set are uniformly statistically processed. During the statistical process, abnormal order data exceeding the preset abnormal range is removed. Abnormal order data includes non-normal fulfillment data caused by user-initiated rescheduling, address abnormalities, order cancellations, or extreme weather, in order to avoid anomalies. Orders interfere with the overall fulfillment status assessment; subsequently, the system centrally summarizes the last-mile fulfillment timeliness deviations corresponding to the remaining orders and writes them into the timeliness deviation cache queue corresponding to the delivery node in chronological order; further, it performs overall calculation on multiple timeliness deviation values ​​in the cache queue to obtain the overall last-mile fulfillment timeliness deviation index of the delivery node under the current statistical period, which is used to characterize the overall fulfillment pressure status of the current delivery node; when the overall last-mile fulfillment timeliness deviation index continues to increase, it indicates that the delivery node is currently experiencing a trend of accumulated fulfillment delays; when the overall last-mile fulfillment timeliness deviation index continues to decrease, it indicates that the current fulfillment status of the delivery node is stabilizing; the system outputs the obtained overall last-mile fulfillment timeliness deviation index to the subsequent time pressure coefficient calculation module to generate the corresponding reverse traction signal; among which, This indicates the overall last-mile delivery timeliness deviation index at the delivery node. This represents the total number of orders within the statistical period. Indicates the first The aggregation processing of last-mile delivery time deviations for each order means that the last-mile delivery time deviation data of multiple orders generated by the delivery node within a preset statistical period are centrally statistically analyzed and comprehensively calculated to obtain an overall indicator reflecting the current delivery status of the entire delivery node, rather than judging based solely on the delivery status of a single order. In other words, the last-mile delivery time deviations of multiple orders are uniformly summarized, statistically analyzed, and comprehensively calculated to generate a comprehensive time deviation index pair that can characterize the overall delivery status of the delivery node.

[0058] S113, simultaneously obtain the current order queue length and average order dwell time of the warehouse node, and calculate the order dwell status based on a weighted method, represented as:

[0059] ;

[0060] The above methods quantify the fulfillment pressure of delivery nodes and the operational congestion of warehousing nodes, providing basic data support for the subsequent generation of reverse traction signals.

[0061] in, This indicates that the order is in a pending state. This indicates the current order queue length of the warehouse node. This represents the average order dwell time, with the order queue length weighting coefficient. A value of 0.4 to 0.7 is acceptable; this is the weighting coefficient for order dwell time. The weights can be between 0.3 and 0.6, and the sum of the two weighting coefficients is preferably 1. Generally, when a warehouse node is more susceptible to order backlog, the weighting coefficient for order queue length is increased; when a warehouse node is more susceptible to orders that have been unprocessed for a long time, the weighting coefficient for order dwell time is increased. Order queue length primarily reflects the current instantaneous congestion level of the warehouse node, reflecting the short-term resource load pressure on the warehouse node; average order dwell time primarily reflects the continuous backlog of orders in the warehouse node, reflecting the long-term processing efficiency of the warehouse node. Therefore, by assigning different weights to the two indicators, both the real-time congestion state and the continuous dwell state of the warehouse node can be considered, making the calculated order dwell state more consistent with the actual warehouse operation, and improving the accuracy and stability of subsequent resource pre-configuration scheme generation.

[0062] S12 maps the last-mile delivery timeliness deviation to a time pressure coefficient and attenuates and corrects it based on the network distance between the delivery node and the storage node, generating a reverse traction signal;

[0063] S121, the overall last-mile delivery timeliness deviation index obtained in S11 is normalized and mapped, converting the last-mile delivery timeliness deviation into a time pressure coefficient, expressed as:

[0064] ;

[0065] in, This indicates the overall last-mile delivery timeliness deviation index at the delivery node. This indicates the preset maximum timeliness deviation benchmark value. The time pressure coefficient ranges from 0 to 1.5. When the time pressure coefficient is between 0 and 0.5, it indicates that the current fulfillment status of the delivery node is relatively stable, and the last-mile fulfillment pressure is low. When the time pressure coefficient is between 0.5 and 1.0, it indicates that the delivery node faces certain fulfillment pressure, requiring a moderate increase in the pre-allocation intensity of warehousing and distribution resources. When the time pressure coefficient is greater than 1.0, it indicates that the delivery node has experienced significant accumulated fulfillment delays, requiring an increase in the pre-outbound priority of warehousing nodes and the pre-scheduling priority of delivery node capacity. Since the time pressure coefficient is calculated by normalizing the overall last-mile fulfillment timeliness deviation of the delivery node against the preset maximum timeliness deviation benchmark, it essentially reflects the degree of deviation of the current fulfillment pressure of the delivery node from the preset acceptable upper limit of fulfillment pressure. When the time pressure coefficient is close to 0, it indicates that the fulfillment status of the delivery node is normal. As the time pressure coefficient gradually increases, it indicates that the risk of order delays is accumulating. When the time pressure coefficient exceeds 1, it indicates that the actual fulfillment deviation has exceeded the preset acceptable range, requiring intervention through advance scheduling of warehousing and distribution resources to prevent further spread of fulfillment risks.

[0066] S122, obtain the network distance between the delivery node and the storage node, and perform attenuation correction on the time pressure coefficient based on the network distance to obtain the distance-corrected pressure value, expressed as:

[0067] ;

[0068] The S122 section of the system first acquires the geographical location data and transportation route data corresponding to the delivery node and the warehousing node. The transportation route data includes road connections, road lengths, and transportation channel information. Based on the actual transportation path between the warehousing node and the delivery node, the system accumulates the lengths of each road segment along the path to obtain the network distance between the two nodes. This network distance is not the straight-line distance between the two nodes, but rather the cumulative travel distance along the transportation route during the actual delivery process. After acquiring the network distance, the system performs distance attenuation correction on the time pressure coefficient. The current time pressure coefficient is correlated with the network distance. When the network distance between the delivery node and the warehousing node is small, it indicates that the warehousing node can complete cargo response and resource scheduling in a short time. Therefore, a higher proportion of the time pressure coefficient is retained, giving the delivery node a strong reverse traction effect. As the network distance gradually increases, the response delay of the warehousing node to the delivery node increases synchronously. At this point, the system gradually reduces the impact intensity corresponding to the time pressure coefficient, gradually weakening the impact of the fulfillment pressure of long-distance delivery nodes on warehousing resource scheduling. In practice, the time pressure coefficient is progressively adjusted based on network distance; the greater the network distance, the lower the adjusted pressure value; the smaller the network distance, the closer the adjusted pressure value is to the original time pressure coefficient. The adjusted pressure value is used to characterize the actual fulfillment traction strength of the delivery node after distance-based adjustment and serves as the basis for generating subsequent reverse traction signals. This approach avoids long-distance delivery nodes from continuously occupying large amounts of warehousing resources due to excessive local fulfillment pressure, while allowing warehousing nodes to prioritize responding to delivery nodes with shorter transportation distances and higher fulfillment pressure, thus improving the efficiency of warehousing and distribution resource coordination and overall fulfillment stability. This indicates the pressure value after distance correction. This indicates the network distance between delivery nodes and warehousing nodes. Indicates the time-pressure coefficient. This represents the distance attenuation coefficient, ranging from 0.001 to 0.02. When the logistics network has a large coverage area and long delivery routes, a smaller distance attenuation coefficient is used to avoid excessively rapid attenuation of pressure values ​​at distant delivery nodes. When delivery nodes are concentrated in one area, a larger distance attenuation coefficient is used to improve the system's sensitivity to distance changes. The distance attenuation coefficient is mainly used to control the rate of pressure value attenuation as network distance increases. This represents the natural exponential constant, used to ensure that the pressure value decreases continuously as the network distance increases, making the pressure value change after distance correction smoother and avoiding sudden changes in pressure value caused by changes in network distance, thereby improving the stability and continuity of the reverse traction signal.

[0069] S123, using the distance-corrected pressure value as the reverse traction strength, generates a reverse traction signal, expressed as: ;

[0070] in, Indicates a reverse traction signal. This indicates the pressure value after distance correction. The signal amplification factor ranges from 1.0 to 3.0. When the system needs to increase the sensitivity of delivery node fulfillment pressure to warehouse resource scheduling, the signal amplification factor can be appropriately increased. When the system needs to reduce the impact of local delivery node pressure fluctuations on overall warehouse and distribution coordination, the signal amplification factor can be appropriately decreased. The signal amplification factor is mainly used to adjust the amplification intensity of the distance-corrected pressure value during the generation of the reverse traction signal. A larger signal amplification factor indicates a stronger traction effect of delivery node fulfillment pressure on warehouse node resource scheduling, leading to earlier initiation of pre-outbound operations and capacity pre-scheduling at warehouse nodes. A smaller signal amplification factor indicates a smoother system response to short-term fulfillment fluctuations, which helps avoid frequent resource adjustments. By reasonably setting the signal amplification factor, both the response speed of warehouse and distribution resource scheduling and overall operational stability can be considered.

[0071] The above methods enable the fulfillment pressure at the delivery end to be transmitted in reverse along the warehousing and distribution network to the upstream warehousing nodes, and the traction intensity is dynamically adjusted based on the network distance between the delivery nodes and the warehousing nodes, thereby improving the accuracy of the linkage and adjustment of warehousing and distribution resources.

[0072] S13, the reverse traction signal is used as the forward demand weight and the order stagnation status is used as the reverse constraint factor. These are input into the preset resource matching function for weighted coupling, and the resource pre-configuration scheme is output.

[0073] S131, the reverse traction signal generated in S12 is used as the forward demand weight, and the order retention status obtained in S11 is used as the reverse constraint factor. These are then input into a preset resource matching function for coupled calculation, as shown below:

[0074] ;

[0075] S131 first acquires the reverse traction signal generated in S12 and the order backlog status obtained in S11. The reverse traction signal is used as the forward demand weight on the resource demand side, and the order backlog status is used as the reverse constraint factor on the resource supply side. The reverse traction signal characterizes the current fulfillment pressure intensity of the delivery node, and the order backlog status characterizes the current resource congestion level of the warehousing node. The system performs unified dimensional processing on the reverse traction signal and the order backlog status, and inputs the processed data into the resource matching function for coupled calculation. During the calculation, the system determines the priority of the current delivery node's demand for warehousing and transportation resources based on the reverse traction signal, and then adjusts the resource scheduling intensity based on the order backlog status. When the reverse traction signal is large and the order backlog status is small, the system increases the resource matching... The system calculates resource matching results and increases the number of pre-outbound tasks at warehouse nodes and the pre-scheduled capacity at delivery nodes. When order backlogs are significant, the system lowers the resource matching result value to prevent warehouse nodes from receiving excessive scheduling pressure under high congestion. After calculating the resource matching result value, the system classifies warehouse and delivery nodes into resource levels based on the value. For nodes with higher resource matching result values, pre-outbound task lists and pre-scheduled capacity lists are generated first. For nodes with lower resource matching result values, the resource pre-configuration intensity is reduced or the current resource status is maintained. The generated resource pre-configuration scheme is synchronously sent to the corresponding warehouse and delivery nodes for subsequent warehouse-side outbound operation pre-configuration and delivery-side capacity pre-configuration operations, achieving coordinated adjustment of warehouse and delivery resources. This represents the value of the resource matching result. Indicates a reverse traction signal. This indicates that the order is in a pending state. This represents the forward demand weighting coefficient, ranging from 0.5 to 0.8. When the current system needs to increase the impact of delivery fulfillment pressure on resource scheduling, a larger value is used for the forward demand weighting coefficient; when the current system is more focused on the stable operation of warehouse nodes, a smaller value is used. The forward demand weighting coefficient is mainly used to control the influence of reverse traction signals in the resource matching process. The reverse constraint weight coefficient ranges from 0.2 to 0.5. A larger value is used when the warehouse node is prone to order backlog or resource congestion; a smaller value is used when the warehouse node has high throughput. The reverse constraint weight coefficient is mainly used to control the degree to which order delays affect resource matching results, thereby preventing resource overload on the warehouse node under high load conditions.

[0076] S132, pre-allocate resources between warehousing nodes and delivery nodes based on resource matching result values; when the resource matching result value exceeds the preset resource allocation threshold, increase the pre-outbound priority of warehousing nodes and the capacity scheduling priority of delivery nodes; when the resource matching result value is lower than the preset resource allocation threshold, reduce the intensity of resource pre-configuration.

[0077] The pre-outbound task list for the warehousing node includes orders to be sorted in advance, orders to be packed in advance, and orders to be loaded in advance; the pre-scheduled capacity list for the delivery node includes vehicles to be booked, delivery routes to be assigned, and delivery personnel to be loaded. The preset resource trigger threshold ranges from 0.6 to 1.2. When the resource matching result exceeds the preset resource trigger threshold, it indicates that the current delivery node's fulfillment pressure has reached a level requiring early intervention. The system then initiates pre-outbound operations at the warehousing node and pre-scheduling operations for the delivery node's transportation capacity. When the resource matching result is below the preset resource trigger threshold, it indicates that the overall operation of the current warehousing and distribution system is still within a controllable range. The system maintains the current resource configuration or only performs low-intensity resource pre-configuration operations. The resource matching result comprehensively reflects the coupling relationship between the fulfillment pressure of the delivery node and the resource congestion status of the warehousing node. When the preset resource trigger threshold is low, the system is more sensitive to fulfillment fluctuations and can trigger warehousing and distribution resource pre-scheduling earlier, but it is prone to frequent resource adjustments. When the preset resource trigger threshold is high, resource pre-configuration is only initiated when the fulfillment pressure increases significantly, which is beneficial to improving the stability of resource operation. By reasonably setting the preset resource trigger threshold, the response speed of warehousing and distribution resource scheduling and the overall resource utilization efficiency can be balanced.

[0078] S132, Generate a resource pre-configuration plan based on the resource matching result value, and send the resource pre-configuration plan to the warehousing node and the delivery node simultaneously to drive the subsequent warehousing side outbound operation pre-configuration operation and the delivery side transportation capacity pre-configuration operation.

[0079] S2: According to the resource pre-configuration scheme, before the order arrives at the warehousing node, perform the outbound operation pre-configuration operation on the warehousing side and the transportation capacity pre-configuration operation on the delivery side to obtain the pre-configured warehousing and distribution coordination status.

[0080] S2 specifically includes:

[0081] S21, parse the pre-outbound task list and pre-scheduled transport capacity list in the resource pre-configuration scheme;

[0082] The system receives the resource pre-configuration scheme generated by S13 and performs structured parsing on the pre-outbound task list and pre-scheduled transportation capacity list in the resource pre-configuration scheme. The pre-outbound task list includes the order number, the warehouse node to which the order belongs, the goods information corresponding to the order, the expected outbound time, and the corresponding outbound platform number. The pre-scheduled transportation capacity list includes the delivery vehicle number, driver number, delivery area, expected departure time, and the corresponding last-mile delivery resource information.

[0083] The pre-outbound task list is categorized and aggregated according to the warehousing node to which the order belongs, and the pre-scheduled transportation capacity list is divided into regions according to the delivery area; then, the association mapping relationship between orders and delivery capacity is established, represented as: ;

[0084] By using the above methods, a pre-association between warehousing resource tasks and delivery capacity resources is achieved, providing a unified scheduling basis for subsequent pre-configuration operations on the warehousing side and the delivery side.

[0085] in, Indicates the first One order, Indicates the first One delivery vehicle, Indicates the expected time of order shipment. This indicates the expected departure time of the delivery vehicle. This indicates the mapping relationship between orders and delivery capacity.

[0086] S22, Before the order arrives at the warehousing node, perform the pre-configuration operation of the warehouse-side outbound operation in advance according to the pre-outbound task list;

[0087] S221: Based on the order information in the pre-outbound task list, pre-allocate inventory cache space for the corresponding order within the warehouse node, and generate the corresponding pre-picking task according to the product category and outbound priority of the order; generate picking waves based on the pre-picking task, and allocate the generated picking waves to the corresponding picker terminals.

[0088] Based on the delivery area and transportation batch information corresponding to the order, the outbound waybill is generated in advance and printed and retained. At the same time, based on the delivery area and vehicle loading plan of the order, the outbound platform in the storage node is bound in advance to obtain the reserved platform information corresponding to the order.

[0089] S222, after completing inventory buffer space pre-reservation, picking wave generation, picker assignment, outbound label pre-printing, and outbound platform pre-binding, generates the pre-outbound ready status for the corresponding order, represented as:

[0090] ;

[0091] By employing the above methods, warehousing nodes can complete the preparation of key outbound resources in advance before an order officially enters the outbound stage, thereby shortening the actual outbound waiting time.

[0092] in, This indicates that the goods are ready for pre-shipment. This indicates the pre-reserved status of the inventory cache slots. Indicates the picking wave generation status. This indicates the pre-generation status of the outbound shipping label. This indicates the pre-binding status of the outbound platform.

[0093] S23, based on the pre-scheduled capacity list, perform pre-configuration of delivery-side capacity in advance;

[0094] Based on the delivery area, number of delivery orders, and expected departure time in the pre-scheduled capacity list, delivery vehicles and driver time slots are pre-locked in advance. Specifically, based on the historical delivery demand and vehicle capacity information corresponding to the delivery area, delivery vehicles are allocated to the corresponding orders, and the available delivery time slots for the corresponding drivers are locked.

[0095] Furthermore, based on the order delivery area, delivery distance, and road conditions, delivery routes and delivery time windows are generated in advance; at the same time, based on the order's last-mile delivery area information, last-mile station resources or express locker resources are pre-allocated, and a correspondence between orders and last-mile delivery resources is established.

[0096] After completing the pre-locking of delivery vehicles, pre-locking of driver time slots, pre-generation of delivery routes, and pre-allocation of last-mile delivery resources, the pre-capacity ready status of the corresponding order is generated, represented as:

[0097] ;

[0098] By employing the above methods, delivery nodes can complete the preparation of transportation resources in advance before an order officially enters the delivery stage, thereby improving the efficiency of delivery resource response.

[0099] in, Indicates that the pre-capacity is ready. This indicates that the delivery vehicle is in a pre-locked state. Indicates the pre-generation status of the delivery route. This indicates the pre-generation status of the delivery time window. This indicates the pre-allocation status of last-mile delivery resources.

[0100] S24, the pre-outbound readiness state and the pre-transportation capacity readiness state are obtained by spatiotemporal correlation to obtain the pre-configured warehouse and distribution coordination state;

[0101] Obtain the expected outbound time of orders in the pre-outbound ready state and the expected departure time of delivery vehicles in the pre-capacity ready state, and perform spatiotemporal correlation processing based on the order's delivery area, outbound platform location, and delivery vehicle parking location;

[0102] The warehouse and distribution time sequence matching degree is calculated based on the time difference between the expected order outbound time and the expected delivery vehicle departure time, and is expressed as: ;

[0103] in, Indicates the time-series matching degree of warehousing and distribution. Indicates the expected time of order shipment. This indicates the expected departure time of the delivery vehicle.

[0104] When the warehouse and distribution time sequence matching degree is less than the preset time sequence alignment threshold, it is determined that the order outbound rhythm and the vehicle dispatch rhythm have reached a coordinated state; when the warehouse and distribution time sequence matching degree is greater than the preset time sequence alignment threshold, the system readjusts the order outbound order or the delivery vehicle dispatch order.

[0105] The spatial matching degree of warehousing and distribution is calculated based on the spatial distance between the outbound platform corresponding to the order and the parking area of ​​the delivery vehicle, and the warehousing and distribution time sequence matching degree is combined to generate the warehousing and distribution collaboration status. The warehousing and distribution collaboration status includes the coupling relationship between the expected outbound time and the expected shipment time of the order, which is used to drive the subsequent warehousing and distribution collaboration execution process.

[0106] The timing alignment threshold ranges from 5 to 30. A smaller threshold is used when the frequency of collaborative operations between warehousing and delivery nodes is high and vehicle turnover is fast; a larger threshold is used when the order size at warehousing nodes is large and there are many concentrated shipment batches of vehicles. The timing alignment threshold primarily measures the allowable time deviation between the expected order outbound time and the expected delivery time of the delivery vehicle. A smaller threshold indicates that the system has high requirements for the rhythm of warehousing and distribution collaboration, reducing vehicle waiting time and order delays, and improving the efficiency of warehousing and distribution linkage. A larger threshold indicates that the system has a higher tolerance for short-term scheduling fluctuations, which helps reduce the scheduling costs caused by frequent adjustments to the outbound or vehicle shipment order. By setting the timing alignment threshold appropriately, both the stability of warehousing operations and the timeliness of delivery can be balanced.

[0107] S3: Monitor the actual execution deviation of the pre-configured warehouse and distribution coordination status in real time during the execution process, dynamically correct the resource pre-configuration scheme based on the actual execution deviation, and redeploy the corrected pre-configuration scheme to the warehouse node and the distribution node, so that the warehouse node re-executes the outbound operation pre-configuration operation and the distribution node re-executes the transportation capacity pre-configuration operation, forming a closed-loop iterative optimization.

[0108] S3 specifically includes:

[0109] S31, during the execution of the pre-configured warehouse and distribution coordination status, collects the actual execution deviation in real time at a preset sampling period;

[0110] Specifically, this includes: during the pre-configured warehousing and distribution collaboration process between warehousing and distribution nodes, continuously collecting data on the warehousing-side execution status, distribution-side execution status, and last-mile fulfillment status at a preset sampling period; whereby the warehousing-side execution status includes the actual order outbound time, the distribution-side execution status includes the actual arrival time of the delivery vehicle, and the last-mile fulfillment status includes the actual order fulfillment completion time. Further, the system calculates the outbound timing deviation based on the time difference between the actual order outbound time and the expected order outbound time, expressed as:

[0111] ;

[0112] in, Indicates the deviation in outbound timing. Indicates the actual time the order is shipped out. This indicates the expected time for order shipment.

[0113] The capacity placement deviation is calculated based on the time difference between the actual arrival time and the expected arrival time of the delivery vehicle, and is expressed as:

[0114] ;

[0115] in, This indicates a deviation in the availability of transport capacity. Indicates the actual arrival time of the delivery vehicle; This indicates the expected arrival time of the delivery vehicle.

[0116] The terminal quadratic deviation is calculated based on the time difference between the actual completion time of the order and the preset promised fulfillment timestamp, and is expressed as:

[0117] ;

[0118] in, Indicates the second-order deviation at the end. Indicates the actual time when the order is fulfilled. This indicates a preset commitment fulfillment timestamp.

[0119] Subsequently, the outbound timing deviation, the transportation capacity positioning deviation, and the terminal secondary deviation are all input as actual execution deviations into the subsequent dynamic correction process to reflect the real-time deviation status in the current coordinated execution process.

[0120] S32, when any actual execution deviation exceeds a preset deviation tolerance threshold, a dynamic correction of the current resource pre-configuration scheme is triggered; specifically including:

[0121] S321 continuously monitors outbound timing deviations, capacity positioning deviations, and last-mile secondary deviations. When any actual execution deviation exceeds the corresponding deviation tolerance threshold, it is determined that the current coordination status has deviated from the expected performance, and a dynamic correction process is triggered. The current actual execution deviation is used as feedback input, while simultaneously acquiring the real-time change in the current order backlog status. The real-time change in order backlog status is used to characterize the dynamic changes in the current order backlog at the warehousing node, with a deviation tolerance threshold ranging from 5 to 20. For outbound timing deviations and capacity positioning deviations, the deviation tolerance threshold is 5 to 15; for last-mile secondary deviations, the preferred deviation tolerance threshold is 10 to 20. When the warehousing and distribution coordination system has high requirements for fulfillment timeliness, a smaller deviation tolerance threshold is used; when the logistics network coverage is large or the transportation route is complex, a larger deviation tolerance threshold is used. The deviation tolerance threshold is mainly used to measure whether the actual execution deviations in the warehousing node, distribution node, and last-mile fulfillment process have exceeded the system's acceptable range. When the deviation tolerance threshold is small, execution anomalies in the warehousing and distribution coordination process can be detected, and a dynamic correction process can be triggered in a timely manner, improving the stability of fulfillment timeliness. When the deviation tolerance threshold is large, it has a higher tolerance for short-term fluctuations, which helps to reduce system fluctuations caused by frequent resource rescheduling. By setting corresponding deviation tolerance thresholds for different actual execution deviations, both the response speed of warehousing and distribution coordination scheduling and the overall resource operation stability can be taken into account.

[0122] S322, the corrected reverse traction strength is recalculated based on the actual execution deviation and the real-time changes in the order's delay status, and is expressed as:

[0123] ;

[0124] in, This indicates the corrected reverse traction strength. Indicates the original reverse traction signal. This indicates the actual deviation from the execution. This indicates the real-time changes in the order's pending status. The deviation compensation coefficient ranges from 0.4 to 0.8. When the warehousing and distribution coordination system has high requirements for real-time fulfillment deviation response, a larger value is used to improve the system's ability to correct for reverse traction caused by actual execution deviations. When the system prioritizes overall resource operational stability, a smaller value is used to avoid frequent resource adjustments due to short-term fluctuations. The deviation compensation coefficient is primarily used to control the impact of actual execution deviations during dynamic correction, enabling the warehousing and distribution coordination system to quickly adjust resource scheduling direction based on real-time execution status. The lag status correction coefficient ranges from 0.2 to 0.6. When warehouse nodes are prone to order backlogs or operational congestion, a larger value is used to enhance the constraint effect of real-time changes in order lag status on the reverse traction strength. When warehouse nodes have high order throughput capacity, a smaller value is used to reduce the impact of short-term warehouse fluctuations on resource scheduling results. The lag status correction coefficient is mainly used to adjust the degree of impact of changes in order lag status on the dynamic resource adjustment process, preventing warehouse nodes from continuing to bear excessive scheduling pressure under high load conditions.

[0125] S322, the corrected reverse traction strength and the current order delay status are re-input into the resource matching function for secondary coupling matching, the resource matching result value is recalculated, and the corrected resource pre-configuration scheme is generated based on the recalculated resource matching result value.

[0126] Through the above methods, the warehouse collaboration system can dynamically adjust warehouse and distribution resources based on real-time deviations during the execution process, thereby improving the real-time adaptability of warehouse collaboration scheduling.

[0127] S322 first obtains the corrected reverse traction strength and the current order backlog status, and then performs unified dimensional processing on both. Subsequently, the system uses the corrected reverse traction strength as the resource demand side input and the current order backlog status as the resource constraint side input, inputting them together into the resource matching function for weighted coupling calculation. During resource matching, the system first determines the fulfillment pressure level corresponding to the current delivery node based on the corrected reverse traction strength, and calculates the priority of warehousing resource demand and delivery capacity demand based on the fulfillment pressure level. When the corrected reverse traction strength continues to increase, the system increases the pre-outbound priority of the corresponding order, the pre-locking priority of the delivery vehicle, and the pre-occupancy priority of the last-mile delivery resources; the system then adjusts the resource scheduling intensity based on the current order backlog status. The system performs constraint corrections. When order backlog is significant, the system reduces the intensity of new resource scheduling and restricts warehouse nodes from adding more high-intensity pre-outbound tasks to prevent further congestion. When order backlog is minimal, the system increases resource release capacity, enabling warehouse nodes to handle more time-sensitive orders. After completing the coupled calculation of reverse traction intensity and order backlog, the system generates corresponding resource matching results and recalculates the number of pre-outbound tasks, picking wave priority, number of pre-locked delivery vehicles, delivery route scheduling order, and the number of resources allocated to end-point stations based on these results. A revised resource pre-configuration scheme is generated based on the recalculated resource allocation results and simultaneously distributed to the corresponding warehouse and delivery nodes for dynamic adjustment, thereby achieving real-time closed-loop correction of warehouse and distribution collaborative resources.

[0128] S33 will redeploy the revised pre-configuration scheme to the warehousing and delivery nodes;

[0129] The system receives the revised resource pre-configuration scheme and re-parses the revised pre-outbound task list and the revised pre-scheduled transport capacity list; then the system reissues the revised pre-outbound task list to the corresponding warehouse nodes.

[0130] The warehouse nodes readjust the inventory buffer occupancy status, picking wave execution order, picker task allocation status, and outbound platform binding relationship based on the revised pre-outbound task list. For orders with higher priority, the warehouse nodes execute the picking, packing, and bagging operations of the corresponding orders in advance. For orders with lower priority, the warehouse nodes postpone the outbound operation sequence of the corresponding orders.

[0131] Meanwhile, the revised pre-scheduled capacity list will be reissued to the corresponding delivery nodes. The delivery nodes will readjust the locking status of delivery vehicles, the allocation status of driver time slots, delivery routes, and delivery time windows according to the revised pre-scheduled capacity list. For areas with high fulfillment pressure, delivery nodes will increase the number of pre-scheduled vehicles or lock delivery resources in advance. For areas with reduced fulfillment pressure, delivery nodes will release some of the pre-occupied capacity resources.

[0132] By employing the above methods, the resource scheduling scheme between warehousing nodes and distribution nodes can be dynamically reconstructed, thereby improving the real-time response capability during the collaborative execution of warehousing and distribution.

[0133] S34, repeat the above steps of monitoring, triggering, correcting, redeploying, and re-executing until all actual execution deviations continuously converge within the deviation tolerance threshold, forming a closed-loop iterative optimization; specifically including:

[0134] After redeploying the revised resource pre-configuration plan, the system continues to monitor the execution status of warehousing nodes, delivery nodes, and last-mile fulfillment status in real time according to the preset sampling period, and continuously calculates outbound timing deviation, transportation capacity positioning deviation, and last-mile secondary deviation. When any actual execution deviation exceeds the corresponding deviation tolerance threshold again, the system repeats the dynamic correction process, resource re-matching process, and resource redeployment process; when each actual execution deviation remains below the corresponding deviation tolerance threshold and remains stable over multiple consecutive sampling periods, the system determines that the current warehousing and distribution coordination status has entered a stable convergence state.

[0135] The degree of deviation convergence is calculated based on the actual execution deviation change trend within a continuous sampling period, and is expressed as follows:

[0136] ;

[0137] in, Indicates the degree of convergence of the deviation. Indicates the number of consecutive sampling periods. Indicates the first Actual execution deviation in each sampling period.

[0138] When the degree of deviation convergence is consistently less than the preset convergence threshold, the system terminates the current dynamic correction process and maintains the execution state of the current resource pre-configuration scheme, forming a closed-loop iterative optimization process based on real-time deviation feedback.

[0139] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0140] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for collaborative optimization of logistics resource allocation for integrated warehousing and distribution, characterized in that, Includes the following steps: S1: Collect the last-mile delivery time deviation of the delivery node and the order retention status of the warehousing node, generate a reverse traction signal based on the last-mile delivery time deviation, and couple and match the reverse traction signal with the order retention status to generate a resource pre-configuration scheme. S2: According to the resource pre-configuration scheme, before the order arrives at the warehousing node, perform the outbound operation pre-configuration operation on the warehousing side and the transportation capacity pre-configuration operation on the delivery side to obtain the pre-configured warehousing and distribution coordination status. S3: Monitor the actual execution deviation of the pre-configured warehouse and distribution coordination status in real time during the execution process, dynamically correct the resource pre-configuration scheme based on the actual execution deviation, and redeploy the corrected pre-configuration scheme to the warehouse node and the distribution node, so that the warehouse node re-executes the outbound operation pre-configuration operation and the distribution node re-executes the transportation capacity pre-configuration operation, forming a closed-loop iterative optimization.

2. The method for collaborative optimization of logistics resource allocation for integrated warehousing and distribution as described in claim 1, characterized in that, S1 specifically includes: S11: Calculate the end-of-line fulfillment timeliness deviation by the difference between the end-of-line fulfillment timestamp of the delivery node and the preset promised timeliness, and calculate the order retention status by weighting the order queue length and retention time of the warehousing node; S12: Map the last-mile delivery timeliness deviation to a time pressure coefficient, and perform attenuation correction based on the network distance between the delivery node and the warehousing node to generate the reverse traction signal; S13: The reverse traction signal is used as the forward demand weight and the order stagnation status is used as the reverse constraint factor. The two are input into a preset resource matching function for weighted coupling, and the resource pre-configuration scheme is output. The resource pre-configuration scheme includes the pre-outbound task list of the warehousing node and the pre-scheduled transportation capacity list of the delivery node.

3. The method for collaborative optimization of logistics resource allocation for integrated warehousing and distribution as described in claim 2, characterized in that, S12 specifically includes: Obtain the overall end-of-line delivery timeliness deviation index and the preset maximum timeliness deviation benchmark value of the delivery node, and calculate the time pressure coefficient based on the proportional relationship between the overall end-of-line delivery timeliness deviation index and the preset maximum timeliness deviation benchmark value; Obtain transportation route data corresponding to delivery nodes and warehousing nodes, and calculate network distance based on the cumulative travel distance in the actual transportation path between warehousing nodes and delivery nodes; The time pressure coefficient is gradually corrected based on the network distance to obtain the distance-corrected pressure value; as the network distance increases, the pressure intensity corresponding to the time pressure coefficient decreases. When the network distance decreases, the pressure intensity corresponding to the time pressure coefficient is increased; a reverse traction signal is generated based on the pressure value after distance correction, and the reverse traction signal is used as the forward demand weight input in the subsequent resource matching function; Obtain the current order queue length and average order dwell time of the warehouse node, and calculate the order dwell status by weighting the order queue length and average order dwell time. The overall end-point delivery timeliness deviation index of the delivery node and the order retention status are used as data inputs for the generation of subsequent reverse traction signals.

4. The method for collaborative optimization allocation of logistics resources for integrated warehousing and distribution as described in claim 2, characterized in that, The resource matching function specifically includes: Acquire reverse traction signals and order standby status, and perform unified dimension processing on reverse traction signals and order standby status; The processed reverse traction signal is used as the forward demand weight on the resource demand side, and the processed order stagnation status is used as the reverse constraint factor on the resource supply side. They are input together into the resource matching function for coupled calculation to obtain the resource matching result value. The priority of warehousing resource demand and transportation resource demand corresponding to the delivery node are determined based on the resource matching result value. When the resource matching result value exceeds the preset resource trigger threshold, increase the number of pre-outbound tasks at the warehouse node and the number of pre-scheduled transportation capacity at the delivery node; when the resource matching result value is lower than the preset resource trigger threshold, reduce the resource pre-configuration intensity or maintain the current resource configuration status. Based on the resource matching results, generate a pre-outbound task list for the warehousing node and a pre-scheduled transportation capacity list for the delivery node, and output the pre-outbound task list and the pre-scheduled transportation capacity list together as a resource pre-configuration scheme to the corresponding warehousing node and delivery node.

5. The method for collaborative optimization of logistics resource allocation for integrated warehousing and distribution as described in claim 1, characterized in that, S2 specifically includes: S21, parse the pre-outbound task list and pre-scheduled transport capacity list in the resource pre-configuration scheme; S22, Before the order arrives at the warehousing node, perform the warehousing-side outbound operation pre-configuration operation in advance according to the pre-outbound task list; S23, Based on the pre-scheduled capacity list, perform the pre-configuration operation of the delivery-side capacity in advance; S24, perform spatiotemporal correlation alignment between the pre-outbound readiness state and the pre-transportation readiness state to obtain the pre-configured warehousing and distribution coordination state, which includes the coupling relationship between the expected outbound time and the expected shipment time of the order.

6. The method for collaborative optimization allocation of logistics resources for integrated warehousing and distribution as described in claim 5, characterized in that, The pre-configuration operation for warehouse-side outbound operations specifically includes pre-reserving inventory buffer space, pre-generating picking waves and assigning pickers, pre-printing outbound shipping labels and pre-binding outbound platforms, forming a pre-outbound ready state.

7. A method for collaborative optimization of logistics resource allocation for integrated warehousing and distribution as described in claim 5, characterized in that, The pre-configuration operation of delivery capacity specifically includes pre-locking delivery vehicles and driver time slots, pre-generating delivery routes and time windows, and pre-allocating terminal stations or express locker resources to form a pre-capacity ready state.

8. The method for collaborative optimization allocation of logistics resources for integrated warehousing and distribution as described in claim 1, characterized in that, S3 specifically includes: S31: During the execution of the pre-configured warehouse and distribution coordination state, the actual execution deviation is collected in real time at a preset sampling period; S32: When any of the actual execution deviations exceeds the preset deviation tolerance threshold, a dynamic correction of the current resource pre-configuration scheme is triggered; S33: Redeploy the revised pre-configuration scheme to the warehousing node and the delivery node, so that the warehousing node re-executes the outbound operation pre-configuration operation according to the revised pre-outbound task list, and the delivery node re-executes the capacity pre-configuration operation according to the revised pre-scheduled capacity list; S34: Repeat the above steps of monitoring, triggering, correcting, redeploying, and re-executing until all actual execution deviations continuously converge within the deviation tolerance threshold, forming a closed-loop iterative optimization.

9. A method for collaborative optimization of logistics resource allocation for integrated warehousing and distribution as described in claim 8, characterized in that, The collected actual execution deviations specifically include the outbound timing deviation between the actual outbound time and the expected outbound time of the warehousing node, the capacity positioning deviation between the actual vehicle arrival time and the expected arrival time of the delivery node, and the terminal secondary deviation between the actual fulfillment time and the preset promised time of the terminal node.

10. A method for collaborative optimization of logistics resources for integrated warehousing and distribution as described in claim 8, characterized in that, The dynamic correction specifically includes taking the actual execution deviation as feedback input, combining it with the real-time changes in the current order stagnation status, recalculating the reverse traction signal through a deviation compensation algorithm, and performing secondary coupling matching based on the recalculated reverse traction signal and the current order stagnation status to generate a corrected resource pre-configuration scheme.

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