Multi-modal logistics capacity dynamic scheduling method, device and storage medium for large-scale extraterrestrial facility construction

Abstract

The application discloses a multi-modal logistics capacity dynamic scheduling method and device for large-scale extraterrestrial facility construction and a storage medium, the method aims at the problems of high single rocket transportation cost and long single space elevator construction period, constructs a whole life cycle cost coupling model integrating a cumulative transportation quantity learning curve and a technology maturity evolution law, adopts a discrete global traversal algorithm to perform multi-dimensional weighted optimization on a construction period and a system total cost in a feasible time domain, determines a global optimal time threshold of capacity mode switching, and introduces a physical disturbance model to correct the robustness of a scheduling scheme. The system automatically generates phased dynamic scheduling instructions based on the optimal time threshold, assigns high-frequency rockets to fill the capacity gap in the early construction period, and switches to a space elevator dominant transportation in the operation period. Through the peak-shaving complementary mechanism of heterogeneous capacity, the application solves the multi-objective conflict of transportation cost and engineering period, and realizes the automatic planning and optimization of the logistics scheme of extraterrestrial infrastructure construction.

Description

Technical Field

[0001] This invention relates to the fields of aerospace systems engineering management and intelligent logistics operation and optimization technology, and in particular to a multimodal logistics capacity dynamic scheduling method and system for the construction of large-scale extraterrestrial facilities. Background Technology

[0002] As space technology advances from "deep space exploration" to "extraterrestrial resource development and infrastructure construction," establishing a sustainable lunar-Earth space logistics transportation system has become a core issue of concern for the international space community. This is especially true when considering the construction of ultra-large-scale, long-term extraterrestrial facilities (such as lunar research stations and long-term settlements), which presents severe challenges to the throughput, economic efficiency, and environmental friendliness of material transportation.

[0003] A search revealed a conventional launch vehicle system based on chemical fuel propulsion. This approach boasts high technological maturity and rapid mission response, and is currently the only commercially viable means of extraterrestrial delivery. However, limited by the Tsiolkovsky rocket equations, chemical fuel propulsion has an extremely low payload ratio, resulting in very high unit transportation costs, and it is difficult to achieve significant diminishing returns as the scale of transportation increases. Furthermore, the ultra-high frequency of rocket launches poses a potential environmental burden on the stratosphere.

[0004] A search revealed that a space tethered transportation system based on non-propellant propulsion (such as a space elevator) is also disclosed in related technologies. This type of system uses electrical energy to drive the carrier along a high-strength tether (such as graphene or carbon nanotubes), theoretically reducing the unit transportation cost to one percent of that of traditional rockets, and possessing zero-emission characteristics. However, the physical construction of such systems is extremely challenging, facing inherent drawbacks such as huge initial construction costs, long construction periods, and limited initial carrying capacity.

[0005] Existing research on extraterrestrial logistics planning is mostly limited to static assessments of single transportation modes. For example, it might focus solely on the launch site scheduling of heavy-lift launch vehicles or simply envision the physical parameters of a space elevator. However, during the long-term evolution of large-scale extraterrestrial facility construction, transportation demands are dynamically changing. Current technologies lack a dynamic scheduling mechanism that can deeply couple "high-frequency, agile but high-cost rocket propulsion" with "large-capacity, low-cost but high-time-delay space tethered transportation."

[0006] Specifically, existing technologies cannot effectively resolve the following technical contradictions: the contradiction between solvency and time feasibility (relying solely on rockets results in prohibitive construction costs, while relying solely on space elevators leads to excessively long construction periods); the contradiction between technological evolution and static planning (failing to consider the dynamic impact of rocket learning curves and the evolution of tether system technology maturity on life cycle cost (LCC); and the contradiction between physical disturbances and system robustness (lacking flexible scheduling strategies for random disturbances such as tether swaying and launch failures).

[0007] Therefore, how to utilize the peak-shifting and complementary mechanism of heterogeneous transportation capacity to achieve Pareto optimal allocation of project delivery cycle, economic cost and environmental benefits through multimodal dynamic scheduling has become a key technical problem that urgently needs to be solved in the field of extraterrestrial infrastructure construction. Summary of the Invention

[0008] Purpose of the invention: In view of the above-mentioned existing technologies, a multimodal logistics capacity dynamic scheduling method for the construction of large-scale extraterrestrial facilities is proposed. This method can overcome the technical bottlenecks of high cost of single rocket transportation or excessively long construction cycle of space elevators during the construction of large-scale extraterrestrial facilities. It also addresses the contradiction between ideal planning and physical friction in complex space environments, and achieves Pareto optimal dynamic scheduling between construction period and total system cost.

[0009] Technical solution: A multimodal logistics capacity dynamic scheduling method for large-scale off-site facility construction, comprising:

[0010] Step 1: Collect the total amount of construction materials for extraterrestrial facilities and dual-modal transport capacity parameters, obtain the total demand for target materials, and collect basic data including rocket launch frequency and payload parameters, space elevator construction cost and annual transport capacity limit;

[0011] Step 2: Construct a dual-modal full life cycle cost coupling model based on Leighton's law. Utilize the cumulative payload learning curve and the evolution law of technology maturity to establish dynamic cost functions for rockets and space elevators respectively, forming a coupled cost assessment framework.

[0012] Step 3: Perform a discrete traversal search of duration and cost within the feasible time domain. Within the feasible time domain determined based on the limit capacity, iteratively calculate the total system cost for each candidate duration, with the space elevator taking priority and the rocket filling the gap.

[0013] Step 4: Calculate the time-cost comprehensive score and locate the optimal mode switching inflection point. Normalize the weighted scores of the duration and cost of each candidate project period. By minimizing the comprehensive evaluation function value, determine the global time threshold for achieving Pareto optimality of project period and cost.

[0014] Step 5: Introduce a physical disturbance model to improve the robustness of the scheduling scheme. Based on the global time threshold, generate instructions to drive the rocket to perform high-frequency material resupply during the construction phase, and switch to scheduling instructions to be performed by the space elevator to carry out low-cost bulk logistics transportation during the operation phase.

[0015] Step 6: Output the globally optimal construction schedule and multimodal capacity allocation scheme, integrate the scheduling instructions, and output a planning scheme that includes the total construction period, annual capacity allocation ratio, and phased cost forecasts.

[0016] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method.

[0017] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method.

[0018] Beneficial effects: 1. This invention is the first to realize the peak-shifting complementarity between rocket transportation (high frequency and agility) and space elevator (low consumption and large capacity) throughout the entire life cycle, solving the problem that traditional static planning cannot adapt to the non-linear cost decay caused by technological progress. Compared with a single mode, it can significantly reduce the total cost of deep space logistics.

[0019] 2. By assigning evaluation weights through the entropy weight method, a scientific quantitative trade-off standard is established between construction period and economic input. The discrete global traversal algorithm is used to ensure the location of the global optimal solution, avoiding the risk of heuristic algorithms getting trapped in local optima.

[0020] 3. By integrating the correction mechanism of Markov chain and Monte Carlo simulation, the physical disturbance and random failure risk in the ultra-long-distance transportation process are fully considered. By increasing the strategic buffer period, the risk resistance of the scheduling scheme in complex physical environment is improved. Attached Figure Description

[0021] Figure 1 This is a flowchart of the multimodal logistics capacity dynamic scheduling method of the present invention;

[0022] Figure 2 This is a time distribution diagram for a hybrid project under non-ideal conditions.

[0023] Figure 3 A comparison chart of project durations under different scenarios under non-ideal conditions;

[0024] Figure 4 A cost comparison chart for different models under non-ideal conditions;

[0025] Figure 5 A phased dynamic scheduling capacity allocation diagram;

[0026] Figure 6 Landscape for time-cost Pareto optimization;

[0027] Figure 7 This is a sensitivity analysis diagram of the learning curve for the marginal cost of rockets. Detailed Implementation

[0028] The invention will now be further explained with reference to the accompanying drawings.

[0029] like Figure 1 As shown, a multimodal logistics capacity dynamic scheduling method for large-scale off-site facility construction aims to resolve the cost and time delay contradiction under a single mode through the evolutionary switching of heterogeneous capacity. The specific implementation steps are as follows:

[0030] Step 1: Collect total construction materials and dual-modal transportation capacity parameters for extraterrestrial facilities

[0031] The system first collects the total material requirements for the construction of extraterrestrial facilities through the input interface. At the same time, the basic performance and economic parameters of the following heterogeneous transportation capacity are obtained:

[0032] Among them, the rocket system parameters include the initial unit transportation cost. Physical limit cost lower bound Single load capacity and the highest annual launch frequency of major launch sites worldwide The parameters of the space elevator system include the fixed construction cost of the data acquisition system. Annual transport capacity limit Initial unit operating cost and annual technology maturity decay factor .

[0033] Step 2: Construct a dual-modal lifecycle cost (LCC) coupled model based on Leighton's law.

[0034] This step quantifies the impact of technological evolution and economies of scale on logistics expenditures by establishing a dynamic cost function.

[0035] 1. Rocket dynamic cost modeling: Based on Leighton's law, unit transportation cost With cumulative freight volume It exhibits power-law decay:

[0036]

[0037] in, The learning rate index, determined based on Leitt's law, reflects the speed of technological evolution; This is the set baseline batch shipment volume.

[0038] like Figure 7 The figure shows a sensitivity analysis diagram of the rocket's marginal cost learning curve. The graph uses the total cumulative transported materials as the horizontal axis and the unit transport cost as the vertical axis, displaying cost change curves under different technological evolution rates: aggressive (LR=0.8), standard (LR=0.85), and conservative (LR=0.9). As the graph shows, based on Lett's law, the unit transport cost exhibits a significant nonlinear decay law with increasing cumulative transport volume. This evolution law provides a data foundation for constructing a dual-modal life-cycle cost coupling model for the system.

[0039] 2. Dynamic Cost Modeling of Space Elevator: Using the Technology Maturity Curve to Describe its Unit Operating Cost The exponential decline:

[0040]

[0041] in, This serves as a lower bound for rigid operating costs to ensure the model conforms to the physical logic of long-term operation.

[0042] Step 3: Perform discrete global traversal optimization for project duration and cost.

[0043] Calculate the minimum project duration for dual-mode full-load operation. and single-mode To determine the discrete search interval The optimization module in the feasible time domain Internal discrete search:

[0044] 1. Search area setting: lower bound Determined by the dual-mode full-load operating capability, the formula is: Upper Boundary The theoretical longest construction period relying solely on the space elevator .

[0045] 2. Load Allocation Iteration: For each candidate project period The system prioritizes allocating space elevator capacity. ,margin It was filled by rockets.

[0046] Total Cost Calculation: Integrate the dynamic cost function from step 2 to calculate the total system cost for this project period. This provides a data foundation for subsequent Pareto optimization.

[0047] Step 4: Calculate the comprehensive evaluation score and locate the optimal mode switching threshold.

[0048] Determining Work Option Weights Using the Entropy Weight Method With cost weight Construct a normalized comprehensive evaluation function :

[0049]

[0050] in, and These are the normalized values ​​for the construction period and total cost, respectively. For the candidate project duration calculated iteratively, The minimum project duration within the feasible time domain. It is the difference between the maximum and minimum project duration within the feasible time domain. The candidate construction period The corresponding total system cost, The minimum total system cost within the feasible time domain. The difference between the maximum and minimum total system cost within the feasible time domain is used to determine the globally optimal time threshold, which is the ideal time threshold for achieving a balance between schedule and cost, by locating the minimum point of this function.

[0051] like Figure 6 The diagram shown is a landscape map of the time-cost Pareto optimization. This map visually illustrates the three-dimensional optimization spatial relationship between project duration, total cost, and comprehensive evaluation indicators. During the discrete traversal search within the feasible time domain, the system accurately identifies the global time threshold that achieves Pareto optimality for both project duration and economic cost by locating the minimum point of the comprehensive evaluation indicator within this three-dimensional surface (i.e., the location marked with an asterisk in the diagram).

[0052] Step 5: Introduce a physical disturbance model to improve the robustness of the scheduling scheme.

[0053] Establish a robust correction mechanism for non-ideal conditions in outer space (such as tether swinging, launch failure, etc.):

[0054] 1. Effective capacity attenuation calculation: A Markov chain is used to simulate the operating states of the space elevator (including normal, oscillating, and fault states), and an effective capacity attenuation coefficient is introduced. :

[0055]

[0056] in, For mechanical failure probability, The variance of the swing amplitude of the tethered rope. This is the sensitivity coefficient for effective transport capacity attenuation.

[0057] 2. Reliability decision criteria: at the confidence level The reliability lag time due to non-ideal factors is calculated below.

[0058] This embodiment uses Monte Carlo simulation to perform no fewer than 10,000 simulation iterations to solve for the actual completion time at a 95% confidence level. :

[0059]

[0060] in, Ideal for the carrying capacity of a space elevator For the ideal carrying capacity of the rocket, This is the effective payload capacity attenuation coefficient of the rocket.

[0061] like Figure 2 The figure shows the project duration distribution under hybrid conditions under non-ideal circumstances. The graph uses the number of years required to complete the project (starting from 2050) as the horizontal axis and frequency as the vertical axis, reflecting the probability frequency distribution of the project duration obtained after incorporating a physical disturbance model and undergoing multiple Monte Carlo simulations. The graph clearly indicates the expected completion time and the upper and lower bounds of the 95% confidence interval, providing a reliable criterion for quantifying reliability lag time and subsequently setting a strategic buffer period of 15-20 years.

[0062] 3. Buffer Adjustment: Compare the simulated expected values ​​with the ideal values ​​to quantify the "reliability lag" caused by non-ideal factors. Add a strategic buffer period of 15-20 years to the ideal threshold to revise the final capacity scheduling plan.

[0063] Step 6: Generate final scheduling instructions and planning scheme

[0064] Based on the revised global time threshold, the system automatically generates phased dynamic scheduling instructions:

[0065] 1. Construction window instructions: Identify the current status of the space elevator's capacity ramp-up, drive high-frequency rockets to perform agile resupply, and fill the initial capacity gap.

[0066] 2. Operational window instruction: When the time threshold is reached, the instruction will automatically switch to the space elevator to perform large-scale low-cost transportation, and the rocket will switch to emergency backup mode.

[0067] Integrate the above instructions to output a planning scheme that includes a completion schedule, annual allocation ratio, and phased cost forecasts.

[0068] like Figure 5The diagram shows the phased dynamic scheduling and allocation of transport capacity. With the construction year on the horizontal axis and the capacity allocation ratio on the vertical axis, the diagram illustrates the dynamic execution process of multimodal transport capacity scheduling commands. In the early stages of construction, the launch vehicles (represented by the red area) bear the main transport capacity for agile resupply; as the project progresses, the proportion of transport dominated by the space elevator (represented by the blue area) gradually increases, and during the operational phase, it achieves low-cost, bulk logistics-led transport of large quantities of materials.

[0069] like Figure 3 and Figure 4 The figures shown are a comparison chart of the construction period and a cost comparison analysis chart under different modes of non-ideal conditions. Figure 3 The engineering completion times of pure space elevator, pure rocket transportation, and the hybrid scheduling mode of this invention were compared. Figure 4 The total life-cycle cost of the three modes was compared. The results show that relying solely on a space elevator would lead to serious delays in construction, while relying solely on rocket transportation would result in extremely high economic costs. The hybrid scheduling mode adopted in this invention effectively neutralizes the shortcomings of a single mode, and achieves the optimal configuration of project delivery cycle and economic cost through a peak-shifting and complementary mechanism.

[0070] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described above.

[0071] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the above-described method.

[0072] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for dynamic scheduling of multimodal logistics capacity for large-scale construction of off-site facilities, characterized in that, include: Step 1: Collect the total amount of construction materials for extraterrestrial facilities and dual-modal transport capacity parameters, obtain the total demand for target materials, and collect basic data including rocket launch frequency and payload parameters, space elevator construction cost and annual transport capacity limit; Step 2: Construct a dual-modal full life cycle cost coupling model based on Leighton's law. Utilize the cumulative payload learning curve and the evolution law of technology maturity to establish dynamic cost functions for rockets and space elevators respectively, forming a coupled cost assessment framework. Step 3: Perform a discrete traversal search of duration and cost within the feasible time domain. Within the feasible time domain determined based on the limit capacity, iteratively calculate the total system cost for each candidate duration, with the space elevator taking priority and the rocket filling the gap. Step 4: Calculate the time-cost comprehensive score and locate the optimal mode switching inflection point. Normalize the weighted scores of the duration and cost of each candidate project period. By minimizing the comprehensive evaluation function value, determine the global time threshold for achieving Pareto optimality of project period and cost. Step 5: Introduce a physical disturbance model to improve the robustness of the scheduling scheme. Based on the global time threshold, generate instructions to drive the rocket to perform high-frequency material resupply during the construction phase, and switch to scheduling instructions to be performed by the space elevator to carry out low-cost bulk logistics transportation during the operation phase. Step 6: Output the globally optimal construction schedule and multimodal capacity allocation scheme, integrate the scheduling instructions, and output a planning scheme that includes the total construction period, annual capacity allocation ratio, and phased cost forecasts.

2. The method according to claim 1, characterized in that, In step 2, the unit transportation cost of the rocket Satisfy the formula ,in For the initial unit transportation cost, This is the lower bound of the physical limit cost. The set baseline batch shipment volume, The learning rate index is determined based on Leighton's law; the unit operating cost of space elevator transportation. Satisfy the formula ,in Initial unit operating cost, As the annual technology maturity decay factor, This represents the lower bound of rigid operating costs.

3. The method according to claim 1, characterized in that, In step 3, the minimum construction period for dual-modal full-load operation is calculated. and single-mode To determine the discrete search interval For each candidate project period The system prioritizes allocating space elevator capacity. ,margin Filled by rockets, of which The total required materials for the construction of extraterrestrial facilities. This represents the annual capacity limit for the space elevator system.

4. The method according to claim 3, characterized in that, In step 3, the minimum construction period Determined by the dual-mode full-load operating capability, the formula is: Single-mode The theoretical longest construction period relying solely on the space elevator ,in This is the maximum annual launch frequency for the rocket system.

5. The method according to claim 1, characterized in that, In step 4, the entropy weight method is used to calculate the work-time weight coefficient. With cost weighting coefficient Construct a comprehensive evaluation function ,in and These are the normalized values ​​of the construction period and the total cost, respectively. The global optimal time threshold is determined by locating the minimum point of this function.

6. The method according to claim 1, characterized in that, In step 5, the Markov chain state space is used to simulate the operation of the space elevator under physical disturbances, and the effective carrying capacity attenuation coefficient is calculated. ,in, For mechanical failure probability, The variance of the tethered swing amplitude. The effective capacity attenuation sensitivity coefficient; the construction period probability distribution is obtained through Monte Carlo simulation, and at a confidence level ( The reliability lag time caused by non-ideal factors is calculated; a strategic buffer period of 15-20 years is added to the global optimal time threshold to correct the final multimodal capacity scheduling scheme.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method of any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method of any one of claims 1 to 6.

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