Computer system, method, and media based on dual-mode logistics pricing and revenue allocation model

Abstract

The purpose of this invention is to provide a computer system, method, and medium based on a dual-mode logistics pricing and revenue distribution model, comprising: a dual-mode logistics scenario construction module for constructing a dual-mode logistics scenario including land and airspace; a cooperative pricing model construction module for obtaining the revenue of a logistics transportation cooperation alliance based on the revenue and costs of the dual-mode logistics scenario; a revenue calculation module for obtaining the maximum revenue of the logistics transportation cooperation alliance and deriving the optimal pricing when the alliance revenue is maximized; and a revenue distribution module for obtaining a correction factor. Shapley The value model determines whether participating entities are satisfied with the returns and obtains the distribution results. This disclosure is beneficial to improving the economy and fairness of the low-altitude last-mile dual-mode logistics system and provides technical support for operational decision-making in relevant scenarios.

Description

Technical Field

[0001] This invention relates to the field of revenue distribution in low-altitude logistics transportation, and in particular to a computer system, method, and medium based on a dual-mode logistics pricing and revenue distribution model. Background Technology

[0002] With the rapid development of e-commerce and smart logistics, the demand for last-mile delivery is continuously rising. Traditional single-mode ground transportation, constrained by factors such as road congestion, increasingly complex delivery scenarios, and ever-growing user demands for timeliness, can no longer simultaneously meet the comprehensive needs of high efficiency, low cost, and high flexibility in last-mile delivery. To overcome the current bottlenecks in last-mile delivery capabilities, leveraging low-altitude airspace resources for air logistics transportation has become one of the core development directions in the low-altitude economy. Currently, the low-altitude last-mile delivery sector generally employs a collaborative operation model combining ground vehicles and low-altitude aircraft to complete the entire delivery process. This dual-mode logistics solution integrates the advantages of ground transportation's wide coverage and strong adaptability with the characteristics of low-altitude transportation's rapid response and precise timeliness control, which can improve last-mile delivery efficiency to a certain extent and further expand the service scope and comprehensive service capabilities of the traditional logistics system.

[0003] Existing research on dual-mode logistics largely focuses on route planning, task scheduling, and transportation efficiency optimization, with a lack of research on service pricing in multi-entity collaborative delivery scenarios. This is especially true in low-altitude last-mile transportation, where significant differences exist among transportation entities in terms of carrying capacity, unit cost, and operational risk. Current technologies have not yet constructed a pricing mechanism that comprehensively reflects the coupling relationship between multiple factors such as price, demand, cost, and risk. Secondly, while existing revenue distribution methods can initially address the issue of revenue sharing in multi-entity cooperation, traditional distribution rules fail to fully reflect the differentiated characteristics of each participant in terms of cost input, service capabilities, risk-bearing, and marginal contribution. When applied to low-altitude dual-mode logistics scenarios, this can easily lead to a mismatch between revenue distribution results and actual contributions, thereby undermining the stability of multi-party cooperation and restricting the system's sustainable operation.

[0004] In summary, existing technologies are insufficient to meet the practical application needs of low-altitude end-point dual-mode logistics systems in terms of service pricing rationality and revenue distribution fairness. Therefore, it is urgent to propose a new pricing and revenue distribution technology solution to make up for the shortcomings of existing technologies. Summary of the Invention

[0005] The purpose of this invention is to provide a computer system, method, and medium based on a dual-mode logistics pricing and revenue distribution model, which is used to solve at least one technical problem in the prior art.

[0006] The technical solution of this invention is:

[0007] A computer system based on a dual-mode logistics pricing and revenue sharing model, oriented towards dual-mode logistics pricing and revenue sharing for low-altitude last-mile delivery, includes:

[0008] A dual-mode logistics scenario construction module is used to construct dual-mode logistics scenarios that include land and airspace; and to define the participating entities and variables involved in the dual-mode logistics scenario.

[0009] The cooperative pricing model construction module interacts with the dual-mode logistics scenario construction module to obtain the revenue of the logistics transportation cooperation alliance based on the revenue and cost of the dual-mode logistics scenario.

[0010] The revenue calculation module interacts with the cooperative pricing model construction module to obtain the maximum revenue of the logistics transportation cooperative alliance, derive the optimal pricing when the alliance revenue is maximized, and obtain the optimal order volume and optimal revenue value based on the optimal pricing.

[0011] The revenue distribution module interacts with the revenue calculation module to use the optimal revenue value as the total alliance profit to be distributed. Based on the optimal pricing and optimal order volume, it obtains the actual contribution of each participating entity in terms of delay risk, order volume, cost input, and incremental value, and generates a correction factor. Then, it uses the correction factor to obtain a modified Shapley value model to distribute the total alliance profit, determines whether the participating entities are satisfied with the revenue, and obtains the distribution result.

[0012] The dual-mode logistics scenario encompassing land and airspace includes:

[0013] Logistics sorting center unit, used for centralized sorting and shipping of goods;

[0014] The relay station unit is connected to the output side of the logistics sorting center unit and is used for cargo transfer and to confirm the target location of any of the cargo.

[0015] A land transportation main unit, connected to the output side of the relay station unit, is used to deliver the goods to the target location using a land transportation route based on the acquired target location; or...

[0016] The airspace transportation main unit is connected to the output side of the relay station unit and is used to deliver the goods to the target location using airspace transportation routes based on the obtained target location.

[0017] The process of obtaining the maximum revenue of the logistics transportation cooperation alliance and deriving the optimal pricing when the alliance revenue is maximized includes:

[0018] Pricing based on logistics and transportation cooperation alliances Construct a revenue function for a logistics and transportation cooperation alliance;

[0019] The optimal pricing is obtained based on the logistics transportation cooperation alliance's revenue function, corresponding to the maximum alliance revenue. :

[0020] ;

[0021] Where L represents the linear relationship between alliance service pricing and delay penalty costs; a represents the daily expected order volume of the logistics and transportation cooperation alliance; β is the enterprise subsidy coefficient; β is the proportion of air transport delivery in the logistics transportation cooperation alliance; b is the price elasticity coefficient of the logistics transportation cooperation alliance; P1 is the probability of severe delay in air transport; P3 is the probability of severe delay in land transport; C α var Ω represents the average variable cost of transporting a single parcel in logistics transportation mode α; α Additional compensation for severe delays in logistics transportation mode α; α = 1 or 2, Represents airspace transportation. It represents land transportation.

[0022] The step of obtaining the optimal order volume and optimal revenue value based on the optimal pricing includes:

[0023] Optimal daily actual order volume under optimal pricing With optimal profit value , respectively represented as:

[0024] ;

[0025] ;

[0026] Among them, C α fix The fixed cost of logistics transportation mode α; α = 1 or 2, Represents airspace transportation. It represents land transportation.

[0027] The revenue function of the logistics and transportation cooperation alliance is expressed as follows:

[0028] ;

[0029] in, Pricing for logistics and transportation cooperation alliances; β represents the daily actual order volume of the logistics transportation cooperation alliance; β represents the proportion of air transport delivery within the logistics transportation cooperation alliance; C α var Let α be the average variable cost of transporting a unit package in logistics transportation mode α. To delay the cost of punishment; Cα fix The fixed cost of logistics transportation mode α; α = 1 or 2, Represents airspace transportation. It represents land transportation.

[0030] The method of using a correction factor to obtain a modified Shapley value model to determine whether participating entities are satisfied with the returns and to obtain the allocation results includes:

[0031] Five positive maintenance factors are constructed, including: delay risk loss, business scale contribution, overall cost input, cooperative alliance value-added, and delivery time cost;

[0032] Normalize any of the aforementioned correction factors to construct a modified Shapley value model;

[0033] The modified Shapley value model is used to obtain the revenue distribution results of any member in the logistics transportation cooperation alliance, and the final distribution result is obtained based on the feedback from the members.

[0034] The modified Shapley value model includes:

[0035] Modified Shapley value of participant t The model is:

[0036] , and ;

[0037] in, This is the adjustment coefficient; The total revenue of the logistics and transportation cooperation alliance N; This refers to the number of participating entities in the logistics and transportation cooperation alliance.

[0038] The normalization process for any of the correction factors to construct the modified Shapley value model includes:

[0039] Any correction factor is dimensionless, positive indicators are standardized, and negative indicators are standardized to obtain the standardized result.

[0040] The objective weights corresponding to the correction factors are determined using the entropy weight method.

[0041] Based on the standardized results and objective weights of any of the aforementioned correction factors, the comprehensive contribution value of any participating entity is obtained, and the allocation coefficient of any participating entity in the alliance revenue is obtained, which is used to construct the modified Shapley value model.

[0042] A dual-mode logistics pricing and revenue sharing method for low-altitude last-mile delivery, based on the aforementioned computer system based on the dual-mode logistics pricing and revenue sharing model, includes:

[0043] Construct a dual-mode logistics scenario that includes land and airspace; and define the participating entities and variables involved in the dual-mode logistics scenario;

[0044] Based on the revenue and costs of the dual-mode logistics scenario, obtain the logistics transportation cooperation alliance revenue;

[0045] Obtain the maximum revenue of the logistics transportation cooperation alliance, derive the optimal pricing when the alliance revenue is maximized, and obtain the optimal order volume and optimal revenue value based on the optimal pricing.

[0046] By using a correction factor to obtain a modified Shapley value model, we can determine whether the participants are satisfied with the returns and obtain the allocation results.

[0047] A computer-readable storage medium:

[0048] The computer-readable storage medium stores a computer program.

[0049] When the computer program is running, it executes the steps of the dual-mode logistics pricing and revenue distribution method for low-altitude last-mile delivery as described above.

[0050] The beneficial effects of the present invention include at least the following:

[0051] The computer system described in this invention establishes a service pricing model based on the differences in transportation capacity, operating costs, and delay risks among different transportation entities in a dual-mode logistics system to determine the logistics service price in the corresponding scenario. Then, it establishes a revenue distribution model applicable to dual-mode collaborative operation scenarios to reasonably distribute the revenue generated from cooperation. The computer system described in this invention is conducive to improving the economy and fairness of low-altitude last-mile dual-mode logistics systems and provides technical support for operational decisions in relevant scenarios.

[0052] The dual-mode logistics pricing and revenue distribution method described in this invention, in a dual-mode logistics scenario encompassing both land and airspace, obtains the revenue of the logistics transportation cooperation alliance and the optimal order volume and revenue value based on the revenue and costs of the dual-mode logistics scenario; and utilizes a five-factor positive adjustment model to obtain a modified Shapley value model to achieve the distribution result. This invention does not directly apply the traditional Shapley value for revenue allocation, but rather addresses the differences between land transportation entities and airspace transportation entities in low-altitude airspace last-mile delivery dual-mode logistics in terms of delay risk losses, business scale contribution, comprehensive cost input, cooperation alliance value-added, and delivery time costs. It introduces multiple positive adjustment factors to modify the basic Shapley value, thus more specifically reflecting the differentiated contributions of each participating entity in this scenario. Attached Figure Description

[0053] Figure 1 This is a system block diagram of the computer system described in this invention;

[0054] Figure 2 yes Figure 1 The diagram illustrates a dual-mode logistics scenario involving both land and airspace.

[0055] Figure 3 This is a flowchart of the dual-mode logistics pricing and revenue distribution method for low-altitude last-mile delivery as described in this invention;

[0056] Figure 4 This is a comparison chart of the revenue distribution results of the dual-mode logistics pricing and revenue distribution method for low-altitude last-mile delivery described in this invention and the traditional independent operation model. Detailed Implementation

[0057] The present application will now be further described with reference to the accompanying drawings.

[0058] In view of the shortcomings of existing technologies in low-altitude airspace end-of-line transportation dual-mode logistics scenarios, such as imperfect service pricing mechanisms and difficulty in accurately reflecting the actual contributions of each participating entity in revenue distribution, the core objective of this invention is to provide a computer system, method, and medium based on a dual-mode logistics pricing and revenue distribution model. Specific Implementation Example 1:

[0060] This disclosure provides an embodiment:

[0061] like Figure 1The core components of the aforementioned computer system include: a dual-mode logistics scenario construction module, a cooperative pricing model construction module, a revenue calculation module, and a revenue distribution module. The dual-mode logistics scenario construction module is used to construct a dual-mode logistics scenario encompassing land and airspace, and to define the participating entities and variables involved in the dual-mode logistics scenario. The cooperative pricing model construction module interacts with the dual-mode logistics scenario construction module to obtain the revenue of the logistics transportation cooperation alliance based on the revenue and costs of the dual-mode logistics scenario. The revenue calculation module interacts with the cooperative pricing model construction module to obtain the maximum revenue of the logistics transportation cooperation alliance, derive the optimal pricing when the alliance revenue is maximized, and obtain the optimal order volume and optimal revenue value based on the optimal pricing. The revenue distribution module interacts with the revenue calculation module to obtain a modified Shapley value model using a correction factor, determine whether the participating entities are satisfied with the revenue, and obtain the distribution result.

[0062] The computer system described in this embodiment establishes a service pricing model based on the differences in transportation capacity, operating costs, and delay risks among different transportation entities in a dual-mode logistics system to determine the logistics service price in corresponding scenarios. Then, it establishes a revenue distribution model suitable for dual-mode collaborative operation scenarios to reasonably allocate the revenue generated from cooperation. This embodiment helps improve the economy and fairness of low-altitude last-mile dual-mode logistics systems and provides technical support for operational decisions in relevant scenarios.

[0063] Specifically, such as Figure 2 The dual-mode logistics scenario construction module includes: a logistics sorting center unit, a relay station unit, and a land transportation main unit or an air transportation main unit. The logistics sorting center unit is responsible for centralized sorting and dispatching of goods; the relay station unit is responsible for goods transshipment; and the destination is used to indicate the user's delivery location. The land transportation main unit, such as vehicles, can transport goods along land routes; the air transportation main unit, such as drones, can transport goods along air routes. The land and air transportation main units together constitute a dual-mode logistics service system, differing in transportation costs, service timeliness, delay risks, and demand response. The aforementioned vehicles and drones form a logistics transportation cooperation alliance, receiving delivery orders from upstream logistics companies, and then jointly completing the delivery task from the sorting center to the destination, with the profits being reasonably distributed within the logistics transportation cooperation alliance.

[0064] Table 1: Variable and Parameter Definition Table

[0065]

[0066] Based on the dual-mode logistics system, the participating entities and variables involved in the logistics transportation cooperation alliance are defined using the parameters defined in Table 1; and after the above parameter definitions, the revenue of the logistics transportation cooperation alliance is calculated.

[0067] In the cooperative pricing model construction module, when the logistics cooperation alliance uses... When setting prices, the alliance's revenue function is:

[0068]

[0069] The revenue function is calculated by summing the revenue from air and land transportation, plus subsidies from the company for air delivery, and then subtracting the fixed costs and delay penalties for both air and land transportation. The actual daily order volume of the logistics transportation alliance is also considered. (Demand Response Function) and Delay Penalty Costs for Parcel Delivery in Logistics and Transportation Partnerships The formula for calculating the penalty cost function is as follows:

[0070] ;

[0071] ;

[0072] In the revenue calculation module, the dependent variable in the revenue function is calculated based on the aforementioned demand response function and penalty cost function. Find the maximum value of (alliance revenue) and derive the optimal pricing when alliance revenue is maximized. :

[0073] ;

[0074] For ease of derivation, the linear relationship between affiliate service pricing and latency penalty costs is integrated into a coefficient L. To ensure the existence and uniqueness of the optimal solution, L must satisfy... The formula is:

[0075] ;

[0076] Best pricing Substituting these values ​​into the demand response function and penalty cost function, we obtain the optimal daily actual order volume under the optimal pricing. With optimal profit value The formula is as follows:

[0077] ;

[0078] .

[0079] The traditional formula for calculating the Shapley value is:

[0080] and ;

[0081] in, Indicating in the game In this context, member t's Shapley value represents their share of the spoils; N represents the set of participants, i.e., all parties involved in the game; V represents the characteristic function, i.e., the value created by each possible sub-alliance; and S represents the sub-alliance. This represents the weighting coefficient. This represents the marginal contribution of member t to alliance S, that is, how much the total value of alliance S increases after member t joins alliance S. This represents the total value of the alliance S containing member t. This represents the value created by the remaining members after member t is removed from the alliance S. For the balance of income and expenditure to be met, the sum of the Shapley values ​​allocated to all members must equal the total value created by all members working together. .

[0082] In the revenue distribution module, in order to more specifically reflect the differentiated contributions of each participating entity in the dual-mode logistics scenario, this embodiment does not directly apply the traditional Shapley value for revenue allocation. Instead, it introduces multiple maintenance positive factors to correct the basic Shapley value, taking into account the differences between land transportation entities and air transportation entities in low-altitude airspace last-mile transportation dual-mode logistics in terms of delay risk loss, business scale contribution, comprehensive cost input, cooperative alliance value-added and delivery time cost.

[0083] Specifically, the aforementioned correction factors include delay risk loss factors, business scale contribution factors, overall cost input factors, cooperative alliance value-added factors, and delivery time cost factors.

[0084] The delay risk loss factor is used to characterize the expected losses caused by delivery delays in last-mile delivery. The calculation formula is as follows:

[0085] ;

[0086] Where t = 1 represents the airspace transport entities in the alliance, t = 2 represents the land transport entities in the alliance, and the delay penalty cost for each transport entity. The calculation formula is as follows:

[0087] ;

[0088] ;

[0089] in, This indicates the actual daily order volume of the airspace transportation entity. This represents the delay penalty cost incurred by the airspace transportation entity during the transportation of each order. This represents the total delay penalty cost for all orders in air transport, and the same applies to land transport. This method is used to quantify the negative impact of each entity on the alliance's revenue.

[0090] The business volume contribution factor measures the actual daily order volume completed by each transport entity during the cooperation period. This factor directly reflects the direct contribution of airspace transport entities and land transport entities to the alliance's total output. The calculation formula is as follows:

[0091] .

[0092] The comprehensive cost input factor reflects the daily costs borne by each transportation entity in fulfilling the cooperation agreement, primarily covering fixed and variable costs. This factor ensures that entities incurring higher costs receive corresponding compensatory compensation. The calculation formula is as follows:

[0093] ;

[0094] The cooperative alliance value-added factor is used to quantify the increase in revenue brought about by each transportation entity switching from a non-cooperative strategy to a cooperative strategy. The calculation formula is as follows:

[0095] ;

[0096] in, This represents the total daily revenue of the airspace transport entities within the cooperative alliance. and This indicates its daily variable costs, delayed delivery costs, and fixed costs. This indicates the daily subsidy provided by the company to the airspace transport entity. This represents the equilibrium revenue of airspace transportation when it is not in cooperation (not cooperating with land transportation, i.e., operating independently). The calculation for land transportation is similar, in order to reflect the added value brought about by the cooperation alliance.

[0097] The delivery time cost factor represents the daily effective service time of each transportation entity, and the calculation formula is as follows:

[0098] and ;

[0099] in, This indicates the proportion of delivery services undertaken by airspace transport entities. Given the number of orders, the time required to complete all orders. Similarly. This indicates the effective daily time range, which must not exceed the maximum daily working hours H.

[0100] To eliminate differences in the dimensions and value ranges of various factors, the aforementioned correction factors are dimensionless, including forward standardization for positive indicators and backward standardization for negative indicators. Then, the entropy weight method is used to determine the objective weights of each correction factor to reduce bias caused by subjective weighting. Based on the standardization results and corresponding weights, the comprehensive contribution value of each participating entity is calculated, and the allocation coefficient of each participating entity in the alliance's revenue is further determined to construct the modified Shapley value revenue distribution model.

[0101] According to the traditional Shapley value calculation formula, the basic Shapley value of the participating subject t is... The allocation coefficient of participant t in the alliance's revenue can be obtained based on the Shapley value correction factor. Then the modified Shapley value of the participating subject t The formula is:

[0102] and ;

[0103] in, For adjustment coefficients, , This represents the total revenue of alliance N. This represents the number of participating entities in the alliance. The modified Shapley value model uses the traditional Shapley value as the base allocation result and performs a zero-sum correction based on the contribution share coefficient of each participating entity. When a participating entity's contribution share coefficient is higher than the average level, its revenue distribution result increases relative to the base Shapley value; when a participating entity's contribution share coefficient is lower than the average level, its revenue distribution result decreases relative to the base Shapley value. At that time, the modified Shapley value degenerates into the traditional Shapley value, as... The increase in the value of the adjustment term enhances the sensitivity of the profit distribution to the contribution share coefficient. Since the adjustment term satisfies the zero-sum property, the total profit of the alliance remains unchanged before and after the adjustment. Furthermore, the alliance can terminate when all members agree on the adjusted distribution plan.

[0104] This embodiment proposes a computer system based on a dual-mode logistics pricing and revenue distribution model. When addressing pricing and revenue distribution in dual-mode logistics for low-altitude last-mile delivery, this system first solves for the optimal alliance pricing and cooperative revenue using a cooperative pricing model within the dual-mode logistics scenario. Then, it distributes revenue based on this cooperative revenue, forming a complete technical chain of "cooperative pricing - cooperative revenue formation - revenue distribution," which better aligns with the actual process of multi-entity collaborative operation in low-altitude airspace last-mile transportation. In the computer system, the cooperative pricing model construction module aims to maximize the total alliance revenue, comprehensively considering factors such as market demand, transportation costs, air freight subsidies, and delay penalties to solve for a unified service price for the alliance. Therefore, it better meets the overall pricing requirements in a dual-mode logistics collaborative operation scenario. Furthermore, the revenue distribution module introduces multiple maintenance positive factors to correct the basic Shapley value, more specifically reflecting the differentiated contributions of each participating entity in this scenario.

[0105] In summary, this embodiment establishes a service pricing model based on the differences in transportation capacity, operating costs, and delay risks among different transportation entities in a dual-mode logistics system to determine logistics service prices in corresponding scenarios. It also establishes a revenue distribution model applicable to dual-mode collaborative operation scenarios to reasonably allocate the revenue generated from cooperation. This embodiment helps improve the economy and fairness of low-altitude last-mile dual-mode logistics systems and provides technical support for operational decisions in relevant scenarios. Specific Implementation Example 2:

[0107] This disclosure provides another embodiment:

[0108] like Figure 3 A dual-mode logistics pricing and revenue distribution method for low-altitude last-mile delivery, based on the computer system described in Specific Embodiment 1 based on the dual-mode logistics pricing and revenue distribution model, includes: constructing a dual-mode logistics scenario encompassing land and airspace; defining the participating entities and variables involved in the dual-mode logistics scenario; obtaining the revenue of the logistics transportation cooperation alliance based on the revenue and costs of the dual-mode logistics scenario; obtaining the maximum revenue of the logistics transportation cooperation alliance and deriving the optimal pricing when the alliance revenue is maximized; obtaining the optimal order volume and optimal revenue value based on the optimal pricing; using a correction factor to obtain a modified Shapley value model, determining whether the participating entities are satisfied with the revenue, and obtaining the distribution result.

[0109] Verification process:

[0110] This embodiment uses the logistics and distribution network of a university town as a specific verification scenario. This scenario covers a geographical area of ​​approximately 18 square kilometers and 10 universities, exhibiting a highly concentrated logistics demand with a daily parcel processing volume of approximately 150,000 pieces. To meet differentiated delivery needs, this embodiment constructs a detailed dual-mode logistics scenario, specifically set as follows: the entire logistics network is divided into three levels, including regional logistics sorting centers (highest level), campus express delivery stations (middle level), and last-mile receiving points (lowest level, mainly student dormitories). A total of two regional logistics sorting centers are planned and deployed across the entire area to coordinate all inbound and outbound parcels. Given the current difficulty in deploying air transport infrastructure at campus express delivery stations, drone equipment is only centrally deployed at the regional sorting centers. Drones take off from the regional sorting centers, performing point-to-point single-stage direct transportation, delivering parcels directly to designated single receiving points within each university, skipping intermediate stations. Land transportation (UGV) involves a two-stage relay transportation, where remaining parcels are delivered using unmanned ground vehicles in a two-stage relay delivery process.

[0111] In the first phase, large unmanned vehicles (UAVs) transport packages in batches from the sorting center to the express delivery stations on each campus. In the second phase, delivery UAVs stationed at each express delivery station complete the "last mile" distribution, delivering packages to the corresponding end-point receiving points. For capacity asset management, all UAVs and UAVs are operated using a leasing model. Under standard configuration, each regional sorting center is equipped with 2 UAVs and 4 unmanned vehicles (UGVs); each campus express delivery station is equipped with 2 UAVs to meet the secondary distribution needs within the campus. The scenario assumes that the daily package demand of each campus is strictly proportional to its population size, covering 10 campuses including Guangdong University of Technology and Sun Yat-sen University, with a total of 16 express delivery stations and 190 end-point receiving points. The demand data for each node is discretized according to the actual population proportion, as shown in Table 2.

[0112] Table 2. Campus Pickup Point Information and Average Daily Parcel Quantity

[0113]

[0114] Regarding the physical parameters of transport capacity and the underlying algorithm planning, this embodiment makes a fine distinction of the attributes of transport vehicles, as shown in Table 3.

[0115] Table 3. Physical Property Parameters of Transport Vehicles

[0116]

[0117] To enhance the robustness and realism of the model, the system introduces a time adjustment factor to scale down the theoretical travel time to simulate non-ideal physical road conditions. A unified maximum daily working time constraint (H=9) is set to filter out timeout scheduling schemes. Simultaneously, an economic environment parameter matrix is ​​pre-defined, including cross-price sensitivity (z=0.5), multi-level delay risk probability, and penalty amount. At the underlying scheduling planning level, the system models the logistics operation process as a capacity-constrained vehicle routing problem (Capacitated VRP). A greedy insertion heuristic algorithm is used, iteratively inserting new receiving points from the nearest unserved node until the load or energy limit is reached, thereby quickly generating a set of feasible paths. During the scheduling and allocation phase, the system executes the longest processing time (LPT) rule, prioritizing the allocation of the longest generated path to the fleet to approximately minimize the maximum completion time (Makespan) of each subsystem. Furthermore, all logistics nodes at each level are equipped with sufficient battery inventory, and delivery vehicles fully adopt replaceable battery technology, effectively eliminating the interference of mid-journey charging waiting time on the total system scheduling time. The specific model input parameter values ​​are shown in Table 4.

[0118] Table 4. Model Input Parameter Values

[0119]

[0120] The experimental comparison and results analysis are as follows:

[0121] This experiment aims to verify the technological advancement of the dual-mode logistics pricing and revenue distribution method for low-altitude last-mile delivery proposed in this embodiment compared to the traditional independent entity (non-cooperative) operation scheme. The core comparison indicators include the system's optimal pricing level, cost, total alliance revenue, and independent entity revenue.

[0122] 1. Comparison and Result Analysis of Pricing Experiments:

[0123] The solution results for the dual-mode logistics cooperative operation scheme and the traditional non-cooperative operation scheme are shown in Table 5 and Table 6, respectively.

[0124] Table 5. Equilibrium Solution Results for Non-Cooperative Operation Schemes

[0125]

[0126] Table 6. Equilibrium Solution Results for Cooperative Operation Scheme

[0127]

[0128] By comparing the equilibrium solution results in Table 5 (non-cooperative operation scheme) and Table 6 (cooperative operation scheme), it can be seen that this embodiment significantly outperforms the traditional non-cooperative scheme in terms of overall economic benefits. Specifically, in the non-cooperative state shown in Table 5, the air transport entity and the land transport entity each pursue their own profit maximization, with daily revenues of RMB 6,912.25 and RMB 189,130.97 respectively, and the overall total revenue of the logistics system is RMB 196,043.22. However, after applying the cooperative pricing model of this invention, as shown in Table 6, the system, as a unified community of interests, successfully jumps to RMB 231,294.85 in total daily revenue after coordination, achieving a significant increase of approximately 18%. This objectively demonstrates the superiority of this embodiment in improving the overall profitability of the system.

[0129] A deeper analysis of its underlying mechanism reveals that the superiority of this embodiment does not stem from the traditional understanding of "blindly expanding market demand," but rather from the global optimization of the system's cost structure resulting from the reconstruction of the underlying pricing mechanism. As shown in Table 5, under the traditional non-cooperative game, the system falls into a suboptimal dilemma. Air transport entities, to cover their high operational and delay risk costs, are forced to set exorbitant prices as high as 8.45 yuan; while land transport entities compete internally with a low price of 3.29 yuan. This fragmented pricing strategy not only leads to the vicious consumption of internal resources but also greatly amplifies the system's "delay penalty cost" due to the high prices set by air transport entities. In contrast, this embodiment, referring to Table 6, breaks down the internal competition barriers, outputting a unified optimal cooperative price of 4.02 yuan through global coordination, and scientifically allocating the air transport capacity share to 11%. Under this unified mechanism, although the total order volume of the system (73,332 orders) is slightly reduced compared to the non-cooperative state (77,775 orders), the unified and appropriate pricing effectively prevents the amplification effect of penalty costs caused by high premiums. In short, this embodiment achieves a significant reduction in global delay penalty costs by proactively stripping away some long-tail orders with low marginal profits. It successfully breaks away from the traditional local optimal solution of working independently and realizes a synergistic qualitative change of "cost reduction and efficiency improvement".

[0130] Comparison and Result Analysis of Profit Distribution Experiments

[0131] Having verified that dual-mode cooperation can effectively increase the total revenue of the alliance, this experiment further compared and verified the revenue distribution mechanism within the system. The revenue distribution results are shown in the following figure. Figure 4 As shown.

[0132] Depend on Figure 4As can be seen from the green bars, compared to their independent operating models, both air and land-based entities can obtain synergistic surpluses regardless of the cooperation allocation method, further confirming the necessity of forming a logistics alliance. However, if only the traditional Shapley value method (blue bars) is used to allocate the cooperative surplus, the profit of the air transport entity (UAV) will surge to 24,538 yuan, while the profit of the land transport entity (UGV) will be 206,757 yuan. The major technical flaw of this traditional allocation scheme is that it mechanically divides the profits based solely on the theoretical marginal contribution at the alliance level, completely ignoring the extreme asymmetry in the underlying physical load, cost input, and risk-bearing within the heterogeneous logistics network. In reality, land-based entities, as the backbone of the network, bear approximately 89% of the actual delivery volume and the main underlying operating costs. The traditional method leads to a severe mismatch between their revenue share and their core contribution, which can easily dampen the cooperative enthusiasm of the massive underlying capacity providers, thereby undermining the stability of the dual-mode logistics alliance from within.

[0133] In contrast, this embodiment, corresponding to the yellow bar chart, fundamentally overcomes the aforementioned deficiency of imbalance of interests. After applying the profit distribution module in this embodiment, the system deeply integrates five dimensions of actual operational objective indicators, scientifically reconstructing the traditional benchmark. For example... Figure 4 As shown, after the correction and restructuring, the profit allocation for the land-based main vehicle (UGV) was reasonably increased to RMB 221,118, accurately matching and compensating for the high costs and heavy load it bears as the backbone of transportation capacity; at the same time, the revenue for the air-based main vehicle (UAV) was scientifically adjusted back to RMB 10,177. Crucially, this adjusted revenue (RMB 10,177) is still significantly higher than the original revenue (RMB 6,912, a relative increase of approximately 47.2%) when the air-based main vehicle operated independently.

[0134] The detailed data comparison above proves that the correction model provided in this embodiment not only accurately corrects allocation deviations and achieves strict equivalence between "risk-input-return", but also perfectly satisfies the "individual rationality constraint" in game theory, that is, ensuring that the benefits of cooperation for each participant are no less than the benefits of working alone.

[0135] In summary, the revenue distribution method provided in this embodiment not only achieves absolute fairness in the internal distribution of the system, but also provides irreplaceable system-level support for building and maintaining a long-term stable low-altitude-land collaborative logistics ecosystem. Specific Implementation Example 3:

[0137] This disclosure also provides an embodiment:

[0138] A computer-readable storage medium storing a computer program; when the computer program is run, it executes the steps of the dual-mode logistics pricing and revenue distribution method for low-altitude last-mile delivery as described in Specific Embodiment 2.

[0139] In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0140] The above descriptions only cover a few specific embodiments of the present invention. However, the present invention is not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention. The above-mentioned serial numbers are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

Claims

1. A computer system based on a dual-mode logistics pricing and revenue distribution model, oriented towards dual-mode logistics pricing and revenue distribution for low-altitude last-mile delivery, characterized in that, include: The dual-mode logistics scenario construction module is used to build dual-mode logistics scenarios that include land and airspace. And define the participating entities and variables involved in the dual-mode logistics scenario; The cooperative pricing model construction module interacts with the dual-mode logistics scenario construction module to obtain the revenue of the logistics transportation cooperation alliance based on the revenue and cost of the dual-mode logistics scenario. The revenue calculation module interacts with the cooperative pricing model construction module to obtain the maximum revenue of the logistics transportation cooperative alliance, derive the optimal pricing when the alliance revenue is maximized, and obtain the optimal order volume and optimal revenue value based on the optimal pricing. The revenue distribution module interacts with the revenue calculation module to use the optimal revenue value as the total alliance profit to be distributed; and obtains the actual contribution of any participating entity based on the optimal pricing and optimal order volume. The Shapley value model is then modified using a correction factor to allocate the total profit of the alliance, and the final allocation result is obtained by combining the satisfaction of the participating entities with the benefits.

2. The computer system based on the dual-mode logistics pricing and revenue distribution model according to claim 1, characterized in that, The dual-mode logistics scenario encompassing land and airspace includes: Logistics sorting center unit, used for centralized sorting and shipping of goods; The relay station unit is connected to the output side of the logistics sorting center unit and is used for cargo transfer and to confirm the target location of any of the cargo. A land transportation main unit, connected to the output side of the relay station unit, is used to deliver the goods to the target location using a land transportation route based on the acquired target location; or... The airspace transportation main unit is connected to the output side of the relay station unit and is used to deliver the goods to the target location using airspace transportation routes based on the acquired target location.

3. The computer system based on the dual-mode logistics pricing and revenue distribution model according to claim 1, characterized in that, The process of obtaining the maximum revenue of the logistics transportation cooperation alliance and deriving the optimal pricing when the alliance revenue is maximized includes: Pricing based on logistics and transportation cooperation alliances Construct a revenue function for a logistics and transportation cooperation alliance; The optimal pricing is obtained based on the logistics transportation cooperation alliance's revenue function, corresponding to the maximum alliance revenue. : ； Where L represents the linear relationship between alliance service pricing and delay penalty costs; a represents the daily expected order volume of the logistics and transportation cooperation alliance; β is the enterprise subsidy coefficient; β is the proportion of air transport delivery in the logistics transportation cooperation alliance; b is the price elasticity coefficient of the logistics transportation cooperation alliance; P1 is the probability of severe delay in air transport; P3 is the probability of severe delay in land transport; C α var Ω represents the average variable cost of transporting a single parcel in logistics transportation mode α; α Additional compensation for severe delays in logistics transportation mode α; α = 1 or 2, where, Represents airspace transportation. It represents land transportation.

4. The computer system based on the dual-mode logistics pricing and revenue distribution model according to claim 3, characterized in that, The step of obtaining the optimal order volume and optimal revenue value based on the optimal pricing includes: Optimal daily actual order volume under optimal pricing With optimal profit value , respectively represented as: ； ； Among them, C α fix The fixed cost of logistics transportation mode α; α = 1 or 2, Represents airspace transportation. It represents land transportation.

5. The computer system based on the dual-mode logistics pricing and revenue distribution model according to claim 3, characterized in that: The revenue function of the logistics and transportation cooperation alliance is expressed as follows: ； in, Pricing for logistics and transportation cooperation alliances; β represents the daily actual order volume of the logistics transportation cooperation alliance; β represents the proportion of air transport delivery within the logistics transportation cooperation alliance; C α var The average variable cost of transporting a unit package in logistics transportation mode α; To delay the cost of punishment; C α fix The fixed cost of logistics transportation mode α; α = 1 or 2, Represents airspace transportation. It represents land transportation.

6. The computer system based on the dual-mode logistics pricing and revenue distribution model according to claim 1, characterized in that, The method of adjusting the Shapley value model using a correction factor to allocate the total profit of the alliance includes: Five positive maintenance factors are constructed, including: delay risk loss, business scale contribution, overall cost input, cooperative alliance value-added, and delivery time cost; Normalize any of the aforementioned correction factors to construct a modified Shapley value model; The modified Shapley value model is used to obtain the revenue distribution results of any member in the logistics transportation cooperation alliance, and the participant's revenue satisfaction is considered to obtain the final distribution result.

7. The computer system based on the dual-mode logistics pricing and revenue distribution model according to claim 6, characterized in that, The modified Shapley value model includes: Modified Shapley value of participant t The model is: , and ; in, This is the adjustment coefficient; The total revenue of the logistics and transportation cooperation alliance N; The number of participating entities in the logistics and transportation cooperation alliance; Base Shapley value; This represents the allocation coefficient of participating entity t in the profits of the logistics and transportation cooperation alliance.

8. The computer system based on the dual-mode logistics pricing and revenue distribution model according to claim 6, characterized in that, The normalization process for any of the correction factors to construct the modified Shapley value model includes: Any correction factor is dimensionless, positive indicators are standardized, and negative indicators are standardized to obtain the standardized result. The objective weights corresponding to the correction factors are determined using the entropy weight method. Based on the standardized results and objective weights of any of the aforementioned correction factors, the comprehensive contribution value of any participating entity is obtained, and the allocation coefficient of any participating entity in the alliance revenue is obtained, which is used to construct the modified Shapley value model.

9. A dual-mode logistics pricing and revenue distribution method for low-altitude last-mile delivery, based on the computer system of the dual-mode logistics pricing and revenue distribution model as described in any one of claims 1-8, characterized in that, include: Construct a dual-mode logistics scenario encompassing both land and airspace; And define the participating entities and variables involved in the dual-mode logistics scenario; Based on the revenue and costs of the dual-mode logistics scenario, obtain the logistics transportation cooperation alliance revenue; Obtain the maximum revenue of the logistics transportation cooperation alliance, derive the optimal pricing when the alliance revenue is maximized, and obtain the optimal order volume and optimal revenue value based on the optimal pricing. A modified Shapley value model was constructed using a correction factor, and the final allocation result was obtained by combining the satisfaction of the participating entities with their returns.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program. When the computer program is run, it executes the steps of the dual-mode logistics pricing and revenue distribution method for low-altitude last-mile delivery as described in claim 9.

Images