A logistics cost management method and system based on shared warehousing resources

Abstract

The application belongs to the technical field of logistics warehouse management, and discloses a logistics cost management method and system based on shared warehouse resources, which comprises the following steps: obtaining task attribute parameters of a to-be-processed logistics order, and determining a single job complexity index of the to-be-processed logistics order; based on the single job complexity index and the current resource occupation state of the shared warehouse system, the resource competition time lag and the additional resource load generated by the to-be-processed logistics order on other tasks in the system are estimated, and a task global influence evaluation value is generated; a resource scheduling instruction is generated according to the task global influence evaluation value; according to the execution result of the resource scheduling instruction, the direct job cost of the to-be-processed logistics order is calculated, and the value of the resource competition time lag and the additional resource load caused by the to-be-processed logistics order is quantified as an indirect influence cost and is associated with the attribution object of the to-be-processed logistics order; the comprehensiveness, accuracy and fairness of cost accounting are realized.

Description

Technical Field

[0001] This application relates to the field of logistics and warehousing management technology, and more specifically, to a logistics cost management method and system based on shared warehousing resources. Background Technology

[0002] In modern logistics operations, the shared warehousing model aggregates dispersed warehousing resources through a unified information platform and flexibly allocates them according to customer needs, effectively reducing enterprises' fixed investment. Initially, this model mainly served enterprises with fixed types of goods and infrequent inbound and outbound operations. The fee calculation method was straightforward, primarily based on the space occupied by the goods and the storage time, and the pay-as-you-go model was widely welcomed.

[0003] However, with the booming development of e-commerce, shared warehousing platforms have attracted a large number of e-commerce companies that sell daily necessities online. These customers are characterized by large order volumes, a wide variety of goods, small order quantities, and extremely high outbound frequency. This results in warehouse operators needing to frequently perform small-batch, multi-variety picking, sorting, and packing operations, significantly increasing labor input and operational complexity within the warehouse.

[0004] To address this change, the platform has added an "operational service surcharge" to its basic "space-time" billing model. This surcharge is typically charged per order or per picked item, aiming to cover the additional labor costs incurred due to frequent, small-batch operations. For example, 0.5 yuan is charged per picked item, or 5 yuan per processed order. The platform believes this surcharge fairly passes on the increased labor costs to the relevant customers.

[0005] However, this simplified "operations service surcharge" has revealed limitations in practice. Vastly different warehouse layouts and product characteristics lead to significant variations in the actual labor input for different picking tasks. For example, processing orders for standard items located near the warehouse entrance and easily accessible differs greatly in time and resource requirements from processing orders for irregularly shaped items located deep within the warehouse on high shelves, requiring forklifts or climbing equipment. Charging the same fixed surcharge for these vastly different orders fails to accurately reflect the actual labor costs incurred. Customers of simple orders may feel their costs are overestimated, while the actual high labor costs of complex orders are not adequately covered, placing additional operational burdens on the platform or warehouse owner.

[0006] A deeper problem lies in the fact that the operational staff in a shared warehousing environment are shared. When a large number of orders from different customers flood in, their complexity and urgency vary. For example, to meet the urgent order needs of a certain customer, the system may prioritize resource allocation, causing other customers' simpler orders to be delayed, affecting fulfillment timelines. If additional staff or overtime is temporarily added to avoid delays, how to fairly distribute the resulting extra labor costs (such as overtime pay) becomes a challenge. Attributing this cost entirely to the customer with the urgent order is inappropriate, because the delays of other orders are also due to the priority allocation of shared resources. Conversely, if overtime pay is distributed equally, it is unfair to customers with simple operational needs who do not incur additional labor burdens, contradicting the "pay-as-you-go" principle of shared warehousing. This problem, caused by the complex or urgent operational needs of specific customers, impacts the allocation of shared labor resources and overall operational efficiency, leading to additional costs, puts existing cost management methods in a predicament.

[0007] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0008] The purpose of this application is to provide a logistics cost management method and system based on shared warehousing resources, aiming to solve the problems that existing cost management methods under the shared warehousing model cannot accurately reflect actual labor costs, are difficult to fairly allocate additional costs caused by complex or urgent operational needs, and thus lead to operational pressure and customer feelings of unfairness.

[0009] Firstly, this application provides a logistics cost management method based on shared warehousing resources, applied to a shared warehousing system, the method comprising:

[0010] A1. Obtain the task attribute parameters of the logistics order to be processed, and determine the individual operation complexity index of the logistics order to be processed based on the task attribute parameters;

[0011] A2. Based on the individual operation complexity index and the current resource occupancy status of the shared warehousing system, estimate the resource contention delay and additional resource load that the priority execution of the pending logistics order will cause to other listed tasks in the system, and generate a global impact evaluation value for the task.

[0012] A3. Generate resource scheduling instructions based on the global impact evaluation value of the task; the resource scheduling instructions are used to control the execution sequence and resource matching relationship of the pending logistics orders and other listed tasks;

[0013] A4. Calculate the direct operating cost of the logistics order to be processed based on the execution result of the resource scheduling instruction;

[0014] A5. Based on the execution result of the resource scheduling instruction, the value corresponding to the resource competition delay and the additional resource load caused by the pending logistics order is quantified as indirect impact cost;

[0015] A6. Associate the direct operating costs and the indirect impact costs with the object to which the pending logistics order belongs.

[0016] Secondly, this application provides a logistics cost management system based on shared warehousing resources, applied to a shared warehousing system, including:

[0017] The complexity assessment module is used to obtain the task attribute parameters of the logistics order to be processed, and determine the individual operation complexity index of the logistics order to be processed based on the task attribute parameters.

[0018] The impact assessment module is used to estimate the resource contention delay and additional resource load that the priority execution of the pending logistics order will cause to other listed tasks in the system, based on the individual operation complexity index and the current resource occupancy status of the shared warehousing system, and generate a global impact assessment value for the task.

[0019] The scheduling module is used to generate resource scheduling instructions based on the global impact evaluation value of the task; the resource scheduling instructions are used to control the execution sequence and resource matching relationship of the pending logistics orders and other listed tasks;

[0020] The direct cost calculation module is used to calculate the direct operating cost of the logistics order to be processed based on the execution result of the resource scheduling instruction.

[0021] The indirect cost calculation module is used to quantify the value of the resource competition delay and the additional resource load caused by the pending logistics order into indirect impact costs based on the execution result of the resource scheduling instruction.

[0022] The cost association module is used to associate the direct operating costs and the indirect impact costs with the object to which the logistics order to be processed belongs.

[0023] Beneficial Effects: This application provides a logistics cost management method and system based on shared warehousing resources. By meticulously assessing the complexity of individual order operations and quantifying their indirect impact on shared resources, it achieves comprehensiveness, accuracy, and fairness in cost accounting. This not only avoids overestimating customer costs for simple orders but also ensures that the actual high labor costs generated by complex orders are fully covered, thereby reducing the operational pressure on platforms or warehouse owners. Furthermore, by quantifying indirect impact costs and linking them to the attributable entities, this application provides a more scientific and reasonable path to realize the "pay-as-you-go" principle under the shared warehousing model, promoting the optimized allocation and efficient utilization of shared warehousing resources. Attached Figure Description

[0024] Figure 1 A flowchart illustrating a logistics cost management method based on shared warehousing resources provided in this application.

[0025] Figure 2 This is a schematic diagram of a logistics cost management system based on shared warehousing resources provided for this application.

[0026] Labeling Explanation: 1. Complexity Assessment Module; 2. Impact Assessment Module; 3. Scheduling Module; 4. Direct Cost Calculation Module; 5. Indirect Cost Calculation Module; 6. Cost Association Module. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0028] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] Please refer to Figure 1 This application discloses a logistics cost management method based on shared warehousing resources, applied to a shared warehousing system. The method includes:

[0030] A1. Obtain the task attribute parameters of the logistics orders to be processed, and determine the individual operation complexity index of the logistics orders to be processed based on the task attribute parameters;

[0031] A2. Based on the individual operation complexity index and the current resource occupancy status of the shared warehousing system, estimate the resource contention delay and additional resource load that priority execution of pending logistics orders will cause to other listed tasks in the system, and generate a global impact evaluation value for the task.

[0032] A3. Generate resource scheduling instructions based on the global impact evaluation value of the task; the resource scheduling instructions are used to control the execution sequence and resource matching relationship of pending logistics orders and other listed tasks;

[0033] A4. Calculate the direct operating costs of pending logistics orders based on the execution results of resource scheduling instructions;

[0034] A5. Based on the execution results of resource scheduling instructions, the value of resource competition delays and additional resource loads caused by pending logistics orders is quantified as indirect impact costs.

[0035] A6. Link direct operating costs and indirect impact costs to the object to which the pending logistics orders belong.

[0036] This application aims to more precisely calculate logistics costs and achieve fair cost allocation by introducing individual operation complexity indicators, task global impact evaluation values, and a quantitative and correlation mechanism for direct operation costs and indirect impact costs, thereby effectively solving the problems existing in the cost management of existing shared warehousing.

[0037] A "shared warehousing system" refers to a platform that integrates multiple warehousing resources (such as storage space, operators, handling equipment, etc.), which can be shared by multiple customers and dynamically allocated according to actual needs.

[0038] "Pending logistics orders" refer to logistics tasks submitted by customers that are waiting for the warehouse system to perform picking, packing, and shipping operations.

[0039] "Task attribute parameters" refers to a series of data that describe the characteristics of logistics orders to be processed, such as the type and quantity of goods in the order, and the urgency level.

[0040] The "Individual Operation Complexity Index" is a comprehensive quantitative indicator that measures the resource input and operational difficulty required to execute a single logistics order.

[0041] "Resource contention delay" refers to the total amount of extra waiting time incurred by other queued tasks while waiting for shared resources due to the priority execution of a certain order.

[0042] "Additional resource load" refers to the extra resources invested to meet specific order requirements (such as expedited or special operations), such as overtime hours and the activation of backup equipment.

[0043] The “Task Global Impact Evaluation Value” is a quantitative assessment of the impact of a pending logistics order on the operational efficiency and cost of the entire shared warehousing system, taking into account resource competition delays and additional resource loads.

[0044] "Resource scheduling instructions" are specific operational instructions generated by the system based on the global impact evaluation value of the task, used to guide the order of order execution and resource allocation.

[0045] "Direct activity costs" refer to the labor and material costs directly incurred in executing a single pending logistics order.

[0046] "Indirect impact costs" refer to the costs that need to be quantified and allocated due to the resource competition delays and additional resource loads caused by pending logistics orders.

[0047] "Attribution" usually refers to the customer who submitted the logistics order to be processed or its corresponding business unit.

[0048] The core of the method proposed in this application lies in the refined management of logistics order costs, and the specific implementation method is as follows:

[0049] In step A1, the task attribute parameters of the logistics orders to be processed are obtained, and the individual operation complexity index of the logistics orders is determined based on these parameters. The task attribute parameters can be obtained in various ways. For example, detailed order information, including product codes, quantities, and customer requirements, can be automatically retrieved from the Order Management System (OMS) via a system interface. Alternatively, operators can manually input relevant parameters according to the specific requirements of the order. When determining the individual operation complexity index, a preset calculation model can be used. This model assigns different weights to different task attribute parameters and then performs a weighted summation. For example, orders containing fragile or large items can be assigned a higher difficulty coefficient; for urgent orders requiring completion within a short time, their urgency coefficient can be increased.

[0050] In step A2, based on the individual operation complexity index and the current resource occupancy status of the shared warehousing system, the resource contention delay and additional resource load on other queued tasks within the system caused by prioritizing the processing of pending logistics orders are estimated, and a global impact assessment value for the task is generated. The current resource occupancy status can be obtained through a real-time monitoring system; for example, the number of currently operating personnel, the number of available devices, and the busy level of each work area can be obtained. The estimation of resource contention delay and additional resource load can employ simulation technology. For example, the system can simulate how prioritizing the processing of this pending logistics order would affect the resource occupancy of other queued or executing tasks under the current resource status, thereby calculating the expected waiting time increment for other tasks. The estimation of additional resource load may include determining whether backup equipment needs to be activated or whether overtime work needs to be arranged. The generation of the global impact assessment value for the task can be achieved by weighted and comprehensive calculation of the estimated resource contention delay and additional resource load to obtain a quantitative evaluation result.

[0051] In step A3, resource scheduling instructions are generated based on the task's global impact assessment value. The generation logic of resource scheduling instructions can be based on preset strategies. For example, if the task's global impact assessment value is low, indicating that the order has little impact on the overall system, a regular insertion instruction can be generated to sort the task according to existing queue rules. If the task's global impact assessment value is high, indicating that prioritizing the execution of the order may have a significant impact on the system, the system can generate more complex scheduling instructions, such as a task splitting instruction to break a large order into multiple smaller orders for batch processing; or a staggered execution instruction to schedule the order for execution during periods of resource idleness; simultaneously, priority adjustment instructions for other queued tasks can also be generated to minimize negative impacts.

[0052] In step A4, the direct operating cost of the pending logistics order is calculated based on the execution result of the resource scheduling instruction. The calculation of direct operating cost can be based on actual operational data. For example, the system can record the actual operation time from the start of picking to the completion of packaging for the order. Then, combined with a preset basic unit-time labor cost and the individual operation complexity index of the order, its direct operating cost is calculated.

[0053] In step A5, based on the execution result of the resource scheduling instruction, the value corresponding to the resource contention delay and additional resource load caused by the pending logistics order is quantified as indirect impact cost. The quantification of indirect impact cost is one of the key innovations of this application. For example, if the priority execution of the pending logistics order causes other tasks to be delayed for a total of 2 hours, and according to the preset delay compensation standard, the delay compensation is 10 yuan per hour, then this part of the indirect impact cost is 20 yuan. If personnel are arranged to work overtime for 2 hours to process the order, and the overtime rate is 30 yuan per hour, then the overtime cost is 60 yuan. If backup equipment is activated, its usage cost is 50 yuan, and this part is also included in the indirect impact cost. Finally, these costs caused by resource contention delay and additional resource load (such as delay loss cost, overtime cost, backup resource cost, etc.) are summarized to obtain the indirect impact cost of the pending logistics order.

[0054] In step A6, direct operating costs and indirect impact costs are associated with the recipient of the pending logistics order. This step aims to achieve fair cost allocation. For example, the calculated direct operating costs and indirect impact costs can be recorded in the order's cost accounting record and associated with the customer ID to which the order belongs. This way, when charging customers, not only direct operating costs can be collected, but also indirect impact costs based on the indirect impact of their orders on the shared warehousing system, thus achieving a more accurate and fairer cost allocation.

[0055] The logistics cost management method based on shared warehousing resources proposed in this application works by constructing a closed-loop management process from order task attributes to final cost accounting. First, by conducting in-depth analysis of the task attribute parameters of each pending logistics order, its individual operational complexity index is quantified, laying the foundation for subsequent resource scheduling and cost accounting. This step addresses the problem of insufficient assessment of order operation difficulty in traditional methods.

[0056] Secondly, at the resource scheduling level, this application introduces the concept of a global task impact assessment value. Before prioritizing the execution of a pending logistics order, the system estimates the potential resource contention delays and additional resource loads it may cause to other listed tasks within the shared warehousing system. This estimation mechanism allows the system to comprehensively consider the impact of a single order on overall operational efficiency and costs, rather than just focusing on its own execution efficiency. Based on the global task impact assessment value, the system can generate intelligent resource scheduling instructions, such as task splitting, off-peak execution, or priority adjustment, thereby optimizing resource allocation and minimizing negative impacts. This step effectively solves the resource conflicts and efficiency degradation problems caused by the lack of global consideration in traditional scheduling.

[0057] Finally, at the cost accounting level, this application divides logistics costs into direct operating costs and indirect impact costs. Direct operating costs are precisely calculated based on actual operation time and order complexity, ensuring an accurate reflection of the operational input for each order. More importantly, this application innovatively quantifies the resource competition delays and additional resource loads caused by pending logistics orders as indirect impact costs. This means that if the priority execution of one order causes delays in other orders or requires additional resource input (such as overtime or activation of backup equipment), the resulting costs will be precisely attributed to the order that caused the problem. Ultimately, by linking these two types of costs to the order's attribution, fair cost allocation is achieved. This mechanism solves the problem of accurately attributing and allocating additional costs arising from competition for shared resources in traditional methods, avoids cost egalitarianism, and allows the "pay-as-you-go" principle to be more thoroughly implemented in a shared warehousing environment.

[0058] Through the aforementioned collaborative efforts, the method of this application enables refined and equitable management of shared warehousing and logistics costs, which not only improves the accuracy of cost accounting but also promotes the optimal allocation of resources and the improvement of overall operational efficiency.

[0059] In some implementations, step A1 includes:

[0060] A101. Obtain the task attribute parameters of the logistics orders to be processed; the task attribute parameters include the overall operation difficulty coefficient, the order composition complexity parameter, and the operation urgency parameter required by the customer;

[0061] A102. Perform a weighted summation of the attribute parameters of each task to obtain the individual job complexity index.

[0062] The task attribute parameters are a set of key indicators used to quantify the inherent complexity of pending logistics orders. Specifically, the comprehensive operational difficulty coefficient reflects the complexity of the handling of various goods within the order; factors such as the size, weight, fragility, and special storage requirements of the goods can all affect operational difficulty. The order composition complexity parameter focuses on the diversity and complexity of the order's internal structure, such as the number of types of goods included, the number of SKUs (stock keeping units), and the complexity of the picking route. The customer-required operational urgency parameter directly reflects the customer's requirements for order processing timeliness, such as whether it is an expedited order and the promised delivery time. These parameters together form the basis for a comprehensive assessment of the operational complexity of a single logistics order.

[0063] Furthermore, weighted summation of the task attribute parameters involves assigning corresponding weights to different parameters based on their impact on the complexity of a single task, then multiplying each parameter value by its weight and summing the results to obtain a comprehensive value, namely the single task complexity index. For example, based on actual business experience or historical data analysis, the relative importance of the comprehensive operational difficulty coefficient, order composition complexity parameter, and operational urgency parameter in assessing the complexity of a single task can be determined, and weights can be set accordingly. This weighted summation method allows for a more objective and accurate quantification of the overall operational complexity of pending logistics orders.

[0064] Through the above technical solution, this application enables a more accurate and comprehensive quantification of the complexity index of individual logistics orders. Compared to simply obtaining task attribute parameters in a general way, this solution clarifies specific task attribute parameters (such as the comprehensive operational difficulty coefficient, order composition complexity parameters, and operational urgency parameters) and uses a weighted summation method. This makes complexity assessment no longer a simple qualitative judgment, but a comprehensive consideration based on multi-dimensional quantitative data. Therefore, it can significantly improve the accuracy and reliability of the individual operational complexity index, providing a more solid data foundation for subsequent resource scheduling, cost estimation, and management, thereby optimizing the utilization efficiency of shared warehousing resources and improving the level of precision in logistics cost management.

[0065] Preferably, step A101 may include:

[0066] For pending logistics orders, the order details are parsed to obtain the number of goods types included in the order, the quantity of each type of goods, the physical characteristic tags of each type of goods, the service request tags, and the expedited tags.

[0067] Based on the physical characteristic labels of each type of goods, obtain the corresponding single-item operation difficulty coefficient, and calculate the comprehensive operation difficulty coefficient by combining the quantity of each type of goods.

[0068] The order complexity parameter is determined based on the number of goods types and service request tags;

[0069] Determine the urgency parameter of the operation based on the urgency marker.

[0070] Analyzing order details involves performing structured analysis on the detailed content of logistics orders to extract information relevant to operational complexity assessment. For example, product codes, quantities, and special requirements can be automatically identified and extracted from the electronic data of the order. The number of product types refers to the quantity of different stock units (SKUs) included in a pending logistics order. The quantity of each product refers to the order quantity of each specific SKU in the order. Physical characteristic labels for each product can include its size, weight, fragility, whether special storage conditions (such as refrigeration or moisture protection) are required, and whether it is a dangerous good. These labels are usually pre-stored in a product database and retrieved through product codes. Service request labels can include whether special packaging, assembly, labeling, or reverse logistics processing are required. Expedited processing indicates a customer's specific requirements for order processing time, such as "same-day delivery" or "next-day delivery."

[0071] The individual item handling difficulty coefficient is a preset indicator based on the physical characteristics of the goods (such as weight, volume, fragility, etc.) to measure the difficulty of handling a single item in the picking, handling, and packaging processes. For example, heavy or fragile goods may have a higher individual item handling difficulty coefficient. The individual item handling difficulty coefficient can be obtained by matching physical characteristic tags in a preset attribute database. The comprehensive handling difficulty coefficient is obtained by multiplying the individual item handling difficulty coefficient of each type of goods by its quantity, and then summing them up for all types of goods. This reflects the total difficulty of handling all the goods in the order.

[0072] The order composition complexity parameter reflects the complexity of the order's internal structure and operational processes. For example, the more types of goods there are, the more complex the picking path may be; the more service request tags there are (such as requiring packaging, assembly, and labeling simultaneously), the more cumbersome the operational process, and the higher the complexity parameter. Specifically, the order composition complexity parameter can be calculated using the following formula: s is the order composition complexity parameter, and k is the number of product types. This is a preset ratio coefficient, where n is the number of service request tags included in the order. The contribution value to the complexity of the i-th service request label (which can be determined by looking up a table).

[0073] The urgency parameter directly reflects the timeliness requirement of an order. The presence of an urgency flag increases the urgency parameter, thus granting it higher priority in subsequent resource scheduling. For example, the urgency parameter can be determined by looking up the urgency flag in a table.

[0074] This application's solution, through in-depth analysis of order details, decomposes abstract task attribute parameters into specific, quantifiable components. By obtaining detailed information such as the number of goods types, the quantity of each type, the physical characteristic tags of each type, service request tags, and expedited markings, it is possible to more comprehensively and accurately assess the inherent complexity and urgency of pending logistics orders. Therefore, based on this refined data, more precise comprehensive operational difficulty coefficients, order composition complexity parameters, and operational urgency parameters can be calculated, thus providing a solid data foundation for determining subsequent individual operation complexity indicators.

[0075] Through the aforementioned technical solution, this application can significantly improve the accuracy and precision of task attribute parameter acquisition. By comprehensively analyzing order details, it avoids complexity assessment biases caused by insufficient information or rough estimations, enabling the determined individual operation complexity indicators to more realistically and comprehensively reflect the actual demand and potential impact of pending logistics orders on shared warehousing resources. This provides more reliable data input for subsequent estimations of resource competition delays and additional resource loads, as well as the calculation of direct operating costs, thereby effectively improving the accuracy and effectiveness of the entire logistics cost management method.

[0076] In some implementations, step A2 includes:

[0077] A201. Using simulation and deduction methods, simulate the execution trajectory of pending logistics orders under the current resource occupancy status based on the individual operation complexity index, in order to obtain the resource occupancy time sequence information of pending logistics orders;

[0078] A202. Based on resource occupancy time sequence information, calculate the total amount of expected waiting time increments for other listed tasks at shared resource nodes caused by prioritizing the processing of pending logistics orders, and use this as the resource contention delay;

[0079] A203. Based on resource usage time sequence information, determine whether the execution of pending logistics orders triggers critical resource bottlenecks and exceeds the normal working time window, and calculate the amount of spare resources activated and the expected overtime hours caused by this, as additional resource load;

[0080] A204. By weighting and combining the resource competition delay and the additional resource load, the overall task impact evaluation value of the logistics orders to be processed is obtained.

[0081] In step A201, the simulation method involves constructing a digital model of the shared warehousing system and inputting parameters such as the individual operational complexity index of the logistics order to be processed and the current resource occupancy status to simulate the actual execution process of the order in the system. This method can employ Discrete Event Simulation (DES) technology to abstract the various operations of the logistics order into a series of events and simulate the sequence of these events and their resource occupancy over time. Alternatively, it can utilize Agent-Based Modeling (ABM) simulation methods, treating operators, equipment, and orders as independent intelligent agents and simulating the entire logistics operation process by defining their interaction rules and behavioral logic. This simulation aims to predict the time points and durations of order occupancy on different resources (such as operators, equipment, and work areas), thereby obtaining the resource occupancy time-series information of the logistics order to be processed. This information records in detail the expected start time, end time, and occupancy duration of the order at each shared resource node, providing accurate time-dimensional data for subsequent resource competition and additional load assessment.

[0082] Furthermore, in step A202, based on the aforementioned resource occupancy timing information, the system can identify the expected waiting time increments that may occur for other listed tasks at specific shared resource nodes (such as operators, equipment, work areas, etc.) due to prioritizing the processing of pending logistics orders. The total amount of these increments is accurately calculated and quantified as resource contention delays, reflecting the potential impact of pending logistics orders on the execution efficiency of other tasks.

[0083] Furthermore, in step A203, by analyzing the aforementioned resource occupancy timing information, the system can determine whether the execution of the pending logistics order will trigger a critical resource bottleneck in the shared warehousing system, such as the capacity of a specific piece of equipment or area reaching its limit. Simultaneously, the system will also determine whether the execution of the order will exceed the normal working time window, such as requiring nighttime or weekend operations. Once a critical resource bottleneck is triggered or the normal working time window is exceeded, the system will calculate the resulting activation of backup resources and the estimated overtime hours. These quantitative indicators collectively constitute the additional resource load, reflecting the additional costs that may be incurred to complete the order.

[0084] Finally, in step A204, the aforementioned resource contention delay and additional resource load are weighted and combined. This means that, based on preset weighting coefficients, the degree of their global impact on the task is quantified and merged to obtain a comprehensive evaluation value of the overall task impact. This evaluation value can comprehensively reflect the combined impact of pending logistics orders on the resource utilization efficiency and operating costs of the entire shared warehousing system when they are executed with priority.

[0085] This application's solution, by introducing simulation and extrapolation methods, can dynamically and accurately predict the resource occupancy sequence of pending logistics orders, thus avoiding errors that may arise from traditional static assessments. By meticulously calculating resource contention delays, this application can quantify the potential delay impact of orders on other tasks, enabling resource scheduling decisions to fully consider overall efficiency. Simultaneously, by identifying critical resource bottlenecks and windows exceeding normal working hours, and quantifying the activation of backup resources and expected overtime hours, this application can comprehensively capture the hidden costs that orders may trigger, ensuring a more accurate assessment of additional resource load. Finally, by weighting and synthesizing resource contention delays and additional resource load, the overall task impact evaluation value can more comprehensively and objectively reflect the comprehensive impact of orders on the overall system operation, providing a more reliable basis for subsequent resource scheduling.

[0086] Through the above technical solutions, this application can significantly improve the accuracy and comprehensiveness of the estimated global impact assessment value of a task. Specifically, by obtaining precise resource occupancy timing information through simulation, the subjectivity and uncertainty of experience-based judgment are avoided; by separately quantifying resource competition delays and additional resource loads, the assessment of order impact becomes more detailed and objective, enabling earlier identification of potential resource conflicts and cost risks. Therefore, the generated global impact assessment value of the task is more reliable, providing stronger data support for subsequent resource scheduling instructions, which helps to achieve optimal allocation of shared warehousing resources and refined management of logistics costs, thereby improving the operational efficiency and economic benefits of the entire shared warehousing system.

[0087] In some implementations, step A3 includes:

[0088] A301. Determine whether the global impact evaluation value of the task exceeds the preset intervention threshold;

[0089] A302. If the limit is exceeded, generate task splitting instructions or off-peak execution instructions for pending logistics orders, and generate priority adjustment instructions for other listed tasks to reduce resource contention delays and additional resource load.

[0090] A303. If the limit is not exceeded, a regular sorting instruction is generated to insert the pending logistics orders into the current job queue.

[0091] Specifically, in step A301, the intervention threshold can be understood as a preset critical value used to measure the degree of global impact that pending logistics orders may have on the resources of the shared warehousing system. When the global impact evaluation value of the task exceeds this threshold, it indicates that the execution of the order may cause significant resource competition or additional load to the system, requiring special scheduling strategies to intervene.

[0092] In step A302, if the global impact evaluation value of the task exceeds the intervention threshold, the system will generate more refined scheduling instructions. For example, a task splitting instruction means breaking down a large pending logistics order into multiple smaller sub-tasks so that they can be processed in parallel at different times or by different resources, thereby distributing resource pressure. A peak-shifting execution instruction means scheduling the execution time of the pending logistics order during periods of lower system resource consumption, such as at night or during off-peak hours, to avoid direct conflicts with existing tasks. Simultaneously, priority adjustment instructions for other listed tasks will also be generated. For example, the priority of some non-urgent tasks may be appropriately reduced to free up resource space for the optimized scheduling of high-impact orders. The purpose is to proactively reduce the resource contention delay and additional resource load caused by prioritizing the processing of the pending logistics order.

[0093] In practical applications, if the global impact evaluation value of the task does not exceed the intervention threshold in step A303, it indicates that the impact of the logistics order to be processed on system resources is within a controllable range and no special intervention is required. At this time, the system will generate a regular sorting instruction to insert the logistics order to be processed into the current job queue according to the preset regular scheduling rules (e.g., first-come-first-served, shortest operation time priority, etc.) to maintain the smoothness of the operation process.

[0094] This application's solution achieves intelligent and adaptive resource scheduling instructions by introducing a mechanism to assess the global impact of tasks. When pending logistics orders may significantly impact system resources, the system can promptly identify and proactively intervene by splitting tasks, staggering execution times, or adjusting priorities, thereby avoiding resource congestion, operational delays, and additional costs caused by blind execution. Conversely, when the task impact is minor, conventional scheduling is used to ensure efficiency. It is precisely this hierarchical and dynamic scheduling strategy that enables the shared warehousing system to more flexibly handle logistics orders of varying sizes and complexities, effectively balancing resource utilization and operational efficiency.

[0095] Through the aforementioned technical solution, this application can dynamically adjust resource scheduling strategies based on the actual impact of pending logistics orders on the resources of the shared warehousing system, avoiding the resource waste or inefficiency that may result from a "one-size-fits-all" scheduling approach. Specifically, by comparing the global impact evaluation value of a task with an intervention threshold, the system can accurately identify orders requiring special handling and adopt strategies such as task splitting, off-peak execution, or priority adjustment, thereby significantly reducing resource competition delays and additional resource load, and effectively controlling indirect impact costs. Therefore, this application not only improves the resource utilization efficiency and operational smoothness of the shared warehousing system but also achieves more refined and cost-effective logistics cost management.

[0096] In some implementations, step A4 includes:

[0097] A401. Based on the execution result of the resource scheduling instruction, obtain the actual operation time of the logistics order to be processed;

[0098] A402. Calculate the direct operation cost based on the actual operation time, the preset basic unit time labor cost, and the individual operation complexity index.

[0099] The execution result of resource scheduling instructions refers to the various data generated after the actual operation of logistics orders according to the generated resource scheduling instructions within the shared warehousing system. Actual operation time refers to the actual time consumed from the start to the completion of the operation for a pending logistics order. This time can be obtained by the system through timestamp recording, sensor data collection, or manual entry. The preset basic unit time labor cost refers to the basic labor cost or equipment operating cost required to complete a unit time operation within the shared warehousing system. This cost is usually preset based on historical data, industry standards, or internal company regulations. The individual operation complexity index is a quantitative indicator measuring the difficulty of operating a single logistics order. The higher the value, the more complex the order operation, potentially requiring more time or higher-skilled labor.

[0100] This application's solution first obtains the actual operation time of the logistics orders to be processed, ensuring the authenticity and accuracy of cost calculation and reflecting the time resources consumed during the actual execution of the orders. Subsequently, it calculates the direct operation cost by combining preset basic unit-time labor costs and individual operation complexity indicators. The introduction of the individual operation complexity indicator allows cost calculation to take into account the differences in operational difficulty among different orders, thus avoiding the bias that may result from simply using time as the sole criterion for cost accounting. For example, a more complex order, even if its operation time is the same as a less complex order, may require more labor input and resource consumption. By introducing the complexity indicator, this difference can be more reasonably quantified, making the calculated direct operation cost more representative.

[0101] The aforementioned technical solution enables refined management of direct operational costs for logistics orders. This solution not only considers the fundamental element of actual operation time but also innovatively incorporates individual operational complexity indicators, allowing cost accounting results to more accurately reflect the actual resource consumption and labor input of each order. This provides shared warehousing systems with more precise cost data, helping companies to make more reasonable pricing, resource allocation, and performance evaluations, thereby improving overall operational efficiency and profitability.

[0102] Specifically, step A402 includes:

[0103] Based on the individual task complexity index, obtain the first adjustment coefficient;

[0104] The preset basic unit time labor cost is adjusted using the first adjustment coefficient to obtain the adjusted unit time labor cost;

[0105] Calculate the direct operating cost based on the actual operating time and the adjusted labor cost per unit time.

[0106] The first adjustment coefficient can be understood as a factor that adjusts the unit time labor cost based on the complexity of the task. Specifically, this first adjustment coefficient can be pre-stored in a mapping table or function, which maps different individual task complexity index values ​​to corresponding adjustment coefficients. For example, when the individual task complexity index is high, the corresponding first adjustment coefficient may be greater than 1, indicating that this type of task requires higher skills or more intensive labor, and therefore should have a higher unit time labor cost; conversely, when the individual task complexity index is low, the first adjustment coefficient may be less than or equal to 1. In this way, the preset basic unit time labor cost is multiplied or weighted by the first adjustment coefficient to obtain an adjusted unit time labor cost that better reflects the actual task complexity. Finally, the calculation of the direct operation cost is based on the product of the actual operation time and the adjusted unit time labor cost.

[0107] This application's solution introduces a first adjustment coefficient and links it to the individual task complexity index, enabling dynamic adjustment of the preset basic unit-time labor cost. Because tasks of varying complexity require different levels of manpower, skills, and effort, this adjustment coefficient reflects the inherent differences in task difficulty in the unit-time labor cost. This transforms cost accounting from a simple average to personalized pricing based on the actual complexity of the task. This mechanism ensures that the labor cost of high-complexity tasks is more fully reflected, avoiding underestimation of the actual input for high-difficulty tasks and overestimation of the cost of low-difficulty tasks, thereby improving the accuracy and rationality of direct operating cost calculation.

[0108] Through the aforementioned technical solution, this application can refine the basic unit-time labor cost based on the actual individual operational complexity index of the logistics orders to be processed, thereby making the calculation of direct operational costs more accurate and fair. This not only helps the shared warehousing system to more rationally evaluate and allocate resources, but also provides customers with a more transparent and realistic cost accounting basis, improving the level of cost management refinement and decision support capabilities.

[0109] In some implementations, step A5 includes:

[0110] A501. Based on the execution results of resource scheduling instructions, obtain data on overtime hours and backup resource activation caused by additional resource load, as well as data on the delay duration of other listed tasks caused by resource contention delays.

[0111] A502. Convert overtime hours data into overtime costs based on the preset unit overtime rate;

[0112] A503. Convert standby resource activation data into standby resource cost based on the preset unit standby resource rate;

[0113] A504. Convert delay duration data into delay loss costs based on preset delay compensation standards;

[0114] A505. The sum of overtime costs, reserve resource costs, and delay loss costs shall be determined as indirect impact costs.

[0115] In step A501, based on the actual execution of resource scheduling instructions, the system monitors and records the usage status of various resources and the execution progress of tasks in real time. Specifically, overtime hours data caused by additional resource load refers to the actual overtime hours incurred by operators due to prioritizing pending logistics orders; backup resource activation data refers to the usage of additional equipment, temporary storage space, and other backup resources actually called upon due to the execution of this order; and delay time data for other listed tasks refers to the actual increase in waiting time incurred by other tasks that are being executed or waiting to be executed due to resource competition. This data is typically collected and aggregated through the shared warehousing system's operation management module, time recording system, and resource monitoring system.

[0116] Further, in step A502, the acquired overtime hour data is multiplied by a preset unit overtime rate to quantify overtime costs. This unit overtime rate is determined based on labor contracts, industry standards, or internal company regulations and is used to measure the additional labor expenditure generated per unit of overtime hours.

[0117] In step A503, the acquired standby resource activation data is multiplied by a preset standby resource rate to quantify the standby resource cost. This standby resource rate is determined based on factors such as the lease cost, depreciation cost, or usage cost of the standby resource and is used to measure the cost incurred per unit of standby resource usage.

[0118] In step A504, the acquired delay duration data is multiplied by a preset delay compensation standard to quantify the delay loss cost. This delay compensation standard can be determined based on a Service Level Agreement (SLA) signed with the customer or an internally defined delay penalty mechanism, and is used to measure the potential losses caused to other customers or systems due to task delays.

[0119] Therefore, in step A505, the overtime cost, spare resource cost, and delay loss cost calculated above are summed to finally determine the indirect impact cost of the logistics order to be processed.

[0120] This application's solution quantifies the resource competition delays and additional resource loads caused by pending logistics orders, converting actual overtime hours, backup resource activation, and delays in other tasks into specific monetary costs. This conversion mechanism clearly reveals indirect impacts that were previously difficult to measure directly, thus providing a solid data foundation for comprehensive logistics cost accounting.

[0121] The aforementioned technical solution enables accurate and comprehensive quantification of indirect costs, avoiding potential biases arising from cost accounting based solely on estimated values. Specifically, by acquiring actual overtime hours, backup resource activation data, and delay duration data, and combining this with relevant rates and standards for calculation, the resulting indirect cost calculations more closely reflect reality. This not only improves the precision of logistics cost management but also provides a more reliable basis for decision-making in resource scheduling, order pricing, and customer service strategy formulation for shared warehousing systems, contributing to optimized overall operational efficiency and cost control.

[0122] Preferably, step A504 may include:

[0123] Retrieve the service level information of the customers to whom other listed tasks belong, corresponding to the delay duration data;

[0124] Based on the service level information, determine the preset delay compensation standards for other listed tasks;

[0125] The delay duration data is converted into delay loss costs based on the established delay compensation standards.

[0126] Specifically, when converting delay duration data into delay loss costs, it is first necessary to obtain the service level information of the customers to whom the corresponding listed tasks belong. Service level information can be understood as the service quality standards agreed upon between the customer and the shared warehousing system. For example, it may include different levels such as "VIP customer," "regular customer," and "economy customer," or levels based on indicators such as response time and delivery accuracy. The purpose is to differentiate the sensitivity of different customers to service delays and their acceptable compensation standards.

[0127] Based on service level information, preset delay compensation standards are determined for other listed tasks. In practice, a mapping table between service levels and delay compensation standards can be pre-established in the system. For example, "VIP customers" may have higher delay compensation standards to reflect their high value and strict requirements for service quality; while "economy customers" may have relatively lower delay compensation standards. The purpose is to dynamically adjust the calculation benchmark for delay compensation according to the customer's actual service level, making the quantification of indirect costs more precise and reasonable.

[0128] Therefore, the delay duration data is converted into delay loss costs based on the established delay compensation standards. This means that for data with the same delay duration, the final calculated delay loss costs may differ depending on the service level of the customer to whom the data belongs.

[0129] This application's solution dynamically determines delay compensation standards by incorporating customer service level information, thus resolving the inaccurate cost assessment issues that may arise from using a single preset standard in traditional solutions. Specifically, when the system obtains delay duration data for other listed tasks, it no longer directly applies a fixed compensation standard. Instead, it first queries the service level information of the customer to which the delayed task belongs. Based on this service level information, the system can obtain a more targeted delay compensation standard that matches the service level from a preset mapping relationship. It is precisely this differentiated compensation standard that allows the calculation of delay loss costs to more accurately reflect the actual sensitivity and potential losses of different customers to service delays, thereby improving the granularity and fairness of indirect cost assessments.

[0130] Through the aforementioned technical solution, this application enables a more refined and personalized quantification of the indirect costs resulting from resource competition delays caused by pending logistics orders. Compared to a basic solution using a single, preset delay compensation standard, this application can dynamically adjust the calculation basis for delay loss costs based on different customer service levels, thereby making the assessment of indirect costs more accurate, reasonable, and fair. This not only helps shared warehousing systems more accurately calculate and allocate logistics costs but also provides customers with differentiated service pricing and compensation strategies, improving customer satisfaction and optimizing the basis for resource scheduling decisions.

[0131] refer to Figure 2 This application also provides a logistics cost management system based on shared warehousing resources, applied to a shared warehousing system, including:

[0132] Complexity assessment module 1 is used to obtain the task attribute parameters of the logistics orders to be processed, and determine the individual operation complexity index of the logistics orders to be processed based on the task attribute parameters (for details, please refer to step A1 above).

[0133] Impact assessment module 2 is used to estimate the resource contention delay and additional resource load that priority execution of pending logistics orders will cause to other listed tasks in the system based on the individual operation complexity index and the current resource occupancy status of the shared warehousing system, and generate a global impact assessment value for the task (for details, please refer to step A2 above).

[0134] The scheduling module 3 is used to generate resource scheduling instructions based on the global impact evaluation value of the task; the resource scheduling instructions are used to control the execution sequence and resource matching relationship of pending logistics orders and other listed tasks (for details, please refer to step A3 above).

[0135] Direct cost calculation module 4 is used to calculate the direct operating costs of logistics orders to be processed based on the execution results of resource scheduling instructions (for details, please refer to step A4 above).

[0136] Indirect cost calculation module 5 is used to quantify the value of resource competition delay and additional resource load caused by pending logistics orders into indirect impact costs based on the execution results of resource scheduling instructions (for details, please refer to step A5 above).

[0137] Cost association module 6 is used to associate direct operating costs and indirect impact costs with the object to which the logistics order to be processed belongs (for details, please refer to step A6 above).

[0138] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A logistics cost management method based on shared warehousing resources, applied to a shared warehousing system, characterized in that, The method includes: A1. Obtain the task attribute parameters of the logistics order to be processed, and determine the individual operation complexity index of the logistics order to be processed based on the task attribute parameters; A2. Based on the individual operation complexity index and the current resource occupancy status of the shared warehousing system, estimate the resource contention delay and additional resource load that the priority execution of the pending logistics order will cause to other listed tasks in the system, and generate a global impact evaluation value for the task. A3. Generate resource scheduling instructions based on the global impact evaluation value of the task; the resource scheduling instructions are used to control the execution sequence and resource matching relationship of the pending logistics orders and other listed tasks; A4. Calculate the direct operating cost of the logistics order to be processed based on the execution result of the resource scheduling instruction; A5. Based on the execution result of the resource scheduling instruction, the value corresponding to the resource competition delay and the additional resource load caused by the pending logistics order is quantified as indirect impact cost; A6. Associate the direct operating costs and the indirect impact costs with the object to which the pending logistics order belongs; Step A2 includes: A201. Using simulation and deduction methods, based on the individual operation complexity index, simulate the execution trajectory of the logistics order to be processed under the current resource occupancy state to obtain the resource occupancy time sequence information of the logistics order to be processed; A202. Based on the resource occupancy timing information, calculate the total amount of the expected waiting time increment of other listed tasks at the shared resource node caused by prioritizing the processing of the pending logistics orders, and use it as the resource contention delay; A203. Based on the resource occupancy timing information, determine whether the execution of the pending logistics order triggers a critical resource bottleneck and exceeds the normal working time window, and calculate the amount of backup resources activated and the expected overtime hours caused by this, as the additional resource load; A204. Weight the resource contention delay and the additional resource load to obtain the global impact evaluation value of the logistics order to be processed; Step A3 includes: A301. Determine whether the global impact evaluation value of the task exceeds a preset intervention threshold; A302. If the limit is exceeded, a task splitting instruction or off-peak execution instruction is generated for the pending logistics order, and a priority adjustment instruction is generated for the other listed tasks to reduce the resource contention delay and the additional resource load. A303. If the limit is not exceeded, a regular sorting instruction is generated to insert the pending logistics order into the current job queue.

2. The logistics cost management method based on shared warehousing resources according to claim 1, characterized in that, Step A1 includes: A101. Obtain the task attribute parameters of the logistics orders to be processed; the task attribute parameters include the comprehensive operation difficulty coefficient, the order composition complexity parameter, and the operation urgency parameter required by the customer; A102. Perform a weighted summation of the attribute parameters of each task to obtain the individual job complexity index.

3. The logistics cost management method based on shared warehousing resources according to claim 2, characterized in that, Step A101 includes: For pending logistics orders, the order details are parsed to obtain the number of goods types included in the order, the quantity of each type of goods, the physical characteristic tags of each type of goods, the service request tags, and the expedited tags. Based on the physical characteristic labels of each type of goods, obtain the corresponding single-item operation difficulty coefficient, and calculate the comprehensive operation difficulty coefficient by combining the quantity of each type of goods. The order complexity parameter is determined based on the number of goods types and service request tags. Based on the urgency marker, the urgency parameter of the operation is determined.

4. The logistics cost management method based on shared warehousing resources according to claim 1, characterized in that, Step A4 includes: A401. Based on the execution result of the resource scheduling instruction, obtain the actual operation time of the logistics order to be processed; A402. Calculate the direct operation cost based on the actual operation time, the preset basic unit time labor cost, and the individual operation complexity index.

5. A logistics cost management method based on shared warehousing resources according to claim 4, characterized in that, Step A402 includes: Based on the single-task complexity index, obtain the first adjustment coefficient; The preset basic unit time labor cost is adjusted using the first adjustment coefficient to obtain the adjusted unit time labor cost; The direct operation cost is calculated based on the actual operation time and the adjusted unit time labor cost.

6. The logistics cost management method based on shared warehousing resources according to claim 1, characterized in that, Step A5 includes: A501. Based on the execution result of the resource scheduling instruction, obtain the overtime hours data and backup resource activation data actually caused by the additional resource load, as well as the delay duration data of other listed tasks actually caused by the resource contention delay; A502. Convert the overtime hour data into overtime costs according to the preset unit overtime rate; A503. Convert the standby resource activation data into standby resource cost according to the preset unit standby resource rate; A504. Convert the delay duration data into delay loss cost according to the preset delay compensation standard; A505. The sum of the overtime cost, the reserve resource cost, and the delay loss cost shall be determined as the indirect impact cost.

7. A logistics cost management method based on shared warehousing resources according to claim 6, characterized in that, Step A504 includes: Obtain the service level information of the customers to whom the other listed tasks belong, corresponding to the delay duration data; Based on the service level information, determine the preset delay compensation standards for other listed tasks; The delay duration data is converted into delay loss costs based on the established delay compensation standards.

8. A logistics cost management system based on shared warehousing resources, applied to a shared warehousing system, characterized in that, include: The complexity assessment module is used to obtain the task attribute parameters of the logistics order to be processed, and determine the individual operation complexity index of the logistics order to be processed based on the task attribute parameters. The impact assessment module is used to estimate the resource contention delay and additional resource load that the priority execution of the pending logistics order will cause to other listed tasks in the system, based on the individual operation complexity index and the current resource occupancy status of the shared warehousing system, and generate a global impact assessment value for the task. The scheduling module is used to generate resource scheduling instructions based on the global impact evaluation value of the task; the resource scheduling instructions are used to control the execution sequence and resource matching relationship of the pending logistics orders and other listed tasks; The direct cost calculation module is used to calculate the direct operating cost of the logistics order to be processed based on the execution result of the resource scheduling instruction. The indirect cost calculation module is used to quantify the value of the resource competition delay and the additional resource load caused by the pending logistics order into indirect impact costs based on the execution result of the resource scheduling instruction. The cost association module is used to associate the direct operating costs and the indirect impact costs with the object to which the logistics order to be processed belongs; When the impact assessment module generates a global impact assessment value for a task, it estimates the resource contention delay and additional resource load that prioritizing the execution of the pending logistics order will cause to other listed tasks in the system, based on the individual task complexity index and the current resource occupancy status of the shared warehousing system. Then, it performs the following: A201. Using simulation and deduction methods, based on the individual operation complexity index, simulate the execution trajectory of the logistics order to be processed under the current resource occupancy state to obtain the resource occupancy time sequence information of the logistics order to be processed; A202. Based on the resource occupancy timing information, calculate the total amount of the expected waiting time increment of other listed tasks at the shared resource node caused by prioritizing the processing of the pending logistics orders, and use it as the resource contention delay; A203. Based on the resource occupancy timing information, determine whether the execution of the pending logistics order triggers a critical resource bottleneck and exceeds the normal working time window, and calculate the amount of backup resources activated and the expected overtime hours caused by this, as the additional resource load; A204. Weight the resource contention delay and the additional resource load to obtain the global impact evaluation value of the logistics order to be processed; When the scheduling module generates resource scheduling instructions based on the global impact evaluation value of the task, it executes: A301. Determine whether the global impact evaluation value of the task exceeds a preset intervention threshold; A302. If the limit is exceeded, a task splitting instruction or off-peak execution instruction is generated for the pending logistics order, and a priority adjustment instruction is generated for the other listed tasks to reduce the resource contention delay and the additional resource load. A303. If the limit is not exceeded, a regular sorting instruction is generated to insert the pending logistics order into the current job queue.

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