Heavy haul railway construction plan compiling method and system considering transport capacity matching

Abstract

This invention provides a method and system for compiling a construction plan for heavy-haul railways that considers capacity matching, relating to the field of computer technology. The method includes: acquiring transportation demand parameters, train technical parameters, maintenance window setting parameters, maintenance window opening cost parameters, and construction and maintenance task parameters within the planning period of the heavy-haul railway; dynamically adapting the maintenance window duration based on the transportation demand parameters, train technical parameters, maintenance window setting parameters, and maintenance window opening cost parameters to obtain a maintenance window duration setting scheme; optimizing the construction task scheduling based on the construction and maintenance task parameters and the maintenance window duration setting scheme to obtain a construction task allocation and scheduling scheme, and calculating the construction task completion rate; and performing a two-layer collaborative iterative optimization based on the construction task completion rate to obtain the final maintenance window setting and construction plan scheme. This invention achieves the synergistic goals of precise guarantee of freight demand, efficient utilization of transportation capacity resources, and orderly progress of construction tasks.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method and system for compiling construction plans for heavy-haul railways that take into account transport capacity matching. Background Technology

[0002] Heavy-haul railways are critical infrastructure for ensuring the transportation of coal from west to east in my country, stabilizing the cross-regional allocation of bulk energy materials, and safeguarding national energy supply security. With the continuous upgrading of my country's energy supply demands, the freight demand of heavy-haul railways exhibits significant phased and temporal fluctuations, placing higher demands on the precision, flexibility, and adaptability of line capacity supply. Line maintenance, repair, and construction work on heavy-haul railways must rely on track closure windows. The allocation of these windows is a key factor influencing line capacity supply and serves as the core link between freight demand, capacity supply, and construction tasks. Currently, heavy-haul railway construction plans and window allocations mostly adopt traditional static planning models, with fixed window durations. The lack of a dynamic linkage mechanism between construction planning and capacity allocation makes it impossible to adapt to the dynamic fluctuations in freight demand. At the same time, the construction and maintenance tasks of heavy-haul railways have complex characteristics, including strict sequential procedures, resource exclusivity, and significant differences in the urgency and priority of different tasks. Priority must be given to repairing critical lines and addressing critical defects. Under the traditional static planning model, the layout of construction plans is not well adapted to the availability of skylight resources. It is impossible to optimize the layout of construction tasks and efficiently allocate resources within the dynamically changing skylight constraints. It is also difficult to simultaneously achieve multiple objectives such as ensuring precise freight demand, efficiently utilizing transport capacity resources, and orderly advancing construction tasks.

[0003] Based on the shortcomings of the existing technologies, there is an urgent need for a method and system for compiling construction plans for heavy-haul railways that takes into account transport capacity matching. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for compiling construction plans for heavy-haul railways that considers transport capacity matching, thereby improving the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows: Firstly, this application provides a method for compiling a heavy-haul railway construction plan that considers capacity matching, including: Obtain parameters for transportation demand, train technical parameters, track maintenance window settings, track maintenance window opening costs, and construction and maintenance tasks during the planning period of heavy-haul railways; Based on the transportation demand parameters, the train technical parameters, the sunroof setting parameters, and the sunroof opening cost parameters, the sunroof duration is dynamically adapted. By constructing and solving the sunroof duration setting model, a daily dynamically adapted sunroof duration setting scheme is obtained within the planning period. Based on the construction and maintenance task parameters and the track maintenance window duration setting scheme, the construction task scheduling is optimized. By constructing and solving the heavy-haul railway construction plan model, the construction task allocation and scheduling scheme is obtained, and the construction task completion rate is calculated. Based on the completion rate of the construction tasks, a two-layer collaborative iterative optimization is performed. The completion rate of the construction tasks is fed back to the track window duration setting model to adjust the track window scheme, and the heavy-haul railway construction plan model is driven to re-schedule tasks and recalculate the completion rate. This process is repeated until the target collaboration converges, and the final track window setting and construction plan scheme is obtained.

[0005] Secondly, this application also provides a heavy-haul railway construction planning system that takes into account capacity matching, including: The acquisition module is used to acquire transportation demand parameters, train technical parameters, track maintenance window setting parameters, track maintenance window opening cost parameters, and construction and maintenance task parameters during the planning period of heavy-haul railways. The adaptation module is used to dynamically adapt the duration of the sunroof based on the transportation demand parameters, the train technical parameters, the sunroof setting parameters, and the sunroof opening cost parameters. By constructing and solving the sunroof duration setting model, the daily dynamically adapted sunroof duration setting scheme within the planning period is obtained. The allocation module is used to optimize the scheduling of construction tasks based on the construction and maintenance task parameters and the track maintenance window duration setting scheme. By constructing and solving the heavy-haul railway construction plan model, the construction task allocation and scheduling scheme is obtained, and the construction task completion rate is calculated. The optimization module is used to perform two-layer collaborative iterative optimization based on the completion rate of the construction tasks. It adjusts the track window scheme by feeding back the completion rate of the construction tasks to the track window duration setting model, and drives the heavy-haul railway construction plan model to re-schedule tasks and recalculate the completion rate. The cycle continues until the target collaboration converges, and the final track window setting and construction plan scheme is obtained.

[0006] The beneficial effects of this invention are as follows: This invention breaks the rigid constraints of the traditional static planning mode for heavy-haul railway construction by constructing a two-layer coupled optimization model that integrates upper-level demand, transport capacity, and track windows, and lower-level track windows and construction micro-scheduling. By using track window duration parameters, the two-layer model achieves strong coupling and linkage, realizing the synergistic goals of precise guarantee of freight demand, efficient utilization of transport capacity resources, and orderly advancement of construction tasks. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a flowchart illustrating a method for compiling a heavy-haul railway construction plan that considers transport capacity matching, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of a heavy-haul railway construction planning system that considers transport capacity matching, as described in an embodiment of the present invention.

[0009] The diagram is labeled as follows: 901, Acquisition Module; 902, Adaptation Module; 903, Allocation Module; 904, Optimization Module. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention 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 the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0011] 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 invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0012] In the actual operation of heavy-haul railways, construction planning faces a complex decision-making environment comprised of a deep interplay between three factors: the strong volatility of transportation demand, the rigid multidimensional constraints of construction tasks, and the high cost and strong limitations of maintenance windows. On the one hand, the transportation demand for bulk energy materials exhibits significant, phased, and spatiotemporally irregular fluctuations, placing extremely high demands on the accuracy of daily line capacity supply. On the other hand, the numerous and diverse maintenance tasks themselves constitute a complex system, with strict sequential processes, resource exclusivity, parallelism, and independence among various temporal coupling relationships, and different tasks having varying safety priorities and urgency levels. Simultaneously, all construction must be carried out during the "maintenance windows" when the line is closed. However, the opening of maintenance windows is subject to multiple constraints, including single window duration, adjacent intervals, the total number of windows in the planning period, and the total duration, directly impacting transport capacity and generating fixed costs, variable costs, and unit time costs. Therefore, the core scenario problem that this patented technical solution aims to solve is: how to dynamically optimize the sunroof opening plan and finely arrange construction tasks through intelligent collaborative decision-making in such a dynamic and multi-constrained environment, so as to simultaneously achieve multiple goals such as prioritizing the supply of transportation capacity on peak demand days, efficiently promoting equipment maintenance on low demand days, and optimizing the construction execution sequence and resource efficiency throughout the entire cycle.

[0013] Example 1: This embodiment provides a method for compiling a construction plan for heavy-haul railways that takes into account transport capacity matching.

[0014] See Figure 1 The figure shows that the method includes steps S100 to S400.

[0015] Step S100: Obtain the transportation demand parameters, train technical parameters, track maintenance window setting parameters, track maintenance window opening cost parameters, and construction and maintenance task parameters during the planning period of the heavy-haul railway. Understandably, step S100 involves the heavy-haul railway dispatching department obtaining various parameters based on transportation organization, line conditions, and technical standards for specific sections, and using this data as input for this method. These parameters include: transportation demand parameters (daily freight demand, train type, operating ratio and load, pure running time and additional time for starting and stopping in ordinary freight car sections, additional time for slow travel, and idle time before maintenance windows); train technical parameters (pure running time in ordinary freight car sections, additional time for starting and stopping, additional time for slow travel, and idle time before maintenance windows); maintenance window setting parameters (range of allowed number of maintenance windows, total duration, opening interval, and traditional fixed maintenance window duration benchmark); maintenance window opening cost parameters (fixed cost per maintenance window, unit duration deviation cost, and unit duration cost); and construction and maintenance task parameters (importance weight of tasks to be constructed in the planning period, duration of individual construction tasks, temporal relationship between tasks, maximum number of concurrent operations allowed at the same time, and maximum number of tasks allowed to be arranged per maintenance window day).

[0016] Step S200: Based on transportation demand parameters, train technical parameters, sunroof setting parameters, and sunroof opening cost parameters, dynamically adapt the sunroof duration. By constructing and solving the sunroof duration setting model, obtain the daily dynamically adapted sunroof duration setting scheme within the planning period. It should be noted that step S2 achieves macro-adaptation of upper-level demand, transport capacity, and track windows by constructing a track window duration setting model that considers the coordination and matching of freight demand, and provides track window duration constraint boundaries for lower-level construction task scheduling.

[0017] Further, step S200 includes steps S210 to S230.

[0018] Step S210: Based on the transportation demand parameters, train technical parameters, sunroof setting parameters, and sunroof opening cost parameters, a collaborative optimization model is constructed. By constructing an objective function system with the first objective of maximizing the average freight demand matching degree during the planning period and the second objective of minimizing the total cost consisting of the fixed cost of sunroof opening, the duration deviation cost, and the unit duration cost, a sunroof duration setting model containing dual optimization objectives is obtained. Specifically, the objective function system includes maximizing the average freight demand matching degree during the planning period and minimizing the total cost objective, which comprises fixed costs, variable costs, and time-based costs. Freight demand matching degree refers to the degree of fit between actual transport capacity and transport demand under the specified time window. Fixed costs are the inevitable expenses incurred when a time window is opened. Variable costs are the costs incurred relative to a fixed base time due to the dynamic changes in time window duration, unlike traditional fixed time windows. Time-based costs are costs proportional to the time window duration. The mathematical expression of the objective function is: ; ; ; ; In the formula, This represents the actual volume transported that day, in ten thousand tons. For the first Daily window duration, in minutes; The unit is minutes of pure travel time for ordinary freight trains within the designated area. Additional time and minutes are added for starting and stopping ordinary freight trucks, in minutes; Time wasted in front of the sunroof, in minutes; Add time and minutes for slow travel, in minutes; No. The variable is set to 1 if it is set and 0 if it is not set. For the first The proportion of similar trains in operation; For the first Train tracking time intervals, in minutes; No. The carrying capacity of this type of train, expressed in tens of thousands of tons; For the first Static load factor for this type of train; For the first The penalty amount can be matched with the demand based on the celestial fortune; The sequence number of the days; Total number of days; For train category serial number; The total number of train categories; This is the maximum duration of the skylight, in minutes. This is the lower limit for the duration of the skylight, in minutes. For the first Daily transportation demand, in units of 10,000 tons; Maximum transportation demand during the planning period, in 10,000 tons; This refers to the penalty coefficient between demand and transport capacity. This represents the total number of days in the planning period, expressed in days. The fixed cost for each sunroof opening is expressed in yuan per opening. Cost per unit duration deviation, expressed in yuan / minute; Cost is expressed per unit of time, in yuan / minute; This is a standard value for the duration of a traditional fixed skylight, expressed in minutes. The primary objective; The second objective is to achieve this.

[0019] The skylight duration setting model also includes constraints, including freight demand constraints, upper and lower limits of skylight duration constraints, interval constraints between adjacent skylights, total number of openings and total opening duration constraints.

[0020] The mathematical expression for the constraint is: ; ; ; ; ; In the formula, This represents the actual volume of goods transported that day. For the first Daily transportation demand; The sequence number of the days; Total number of days; No. Variables are set up for the daily window; This is the lower limit for the duration of the skylight. This is the maximum duration for the skylight. and This represents the maximum and minimum number of days between two consecutive skylights; For the first time during the planning period The date sequence number of the first time a skylight was opened; For the first time during the planning period The date sequence number of the first skylight opening; This refers to the total number of times the skylight was opened during the planning period; and This represents the minimum and maximum total number of times a skylight is allowed to be opened during the planning period; and This represents the maximum and minimum total cumulative duration of all skylights during the planning period; For the first The daily skylight duration constraint system comprises five dimensions: First, there's the capacity sufficiency constraint, mandating that the actual transport capacity of the line after a daily skylight opening must not be less than the day's freight demand—this is fundamental to ensuring energy transport. Second, there's the skylight duration range constraint, stipulating that if a skylight is opened on a given day, its duration must be between a preset minimum and maximum value; if not opened, the duration is zero. Third, there's the skylight opening interval constraint, controlling the number of days between two adjacent skylights, requiring them to remain within the specified minimum and maximum intervals to avoid overly dense or sparse construction. Fourth, there's the total number of skylight openings constraint, meaning the total number of skylight openings throughout the entire planning period cannot exceed the allowable minimum and maximum values. Finally, there's the total skylight duration constraint, macroscopically controlling the cumulative duration of all skylights within the planning period, which must fall within the specified upper and lower limits of total duration. These constraints, from micro to macro, collectively constitute the rigid boundaries for the allocation of skylight resources in the time dimension.

[0021] Step S220: Set up the model according to the skylight duration and solve for feasible solutions. By searching the solution space of the model for feasible solutions that simultaneously satisfy the objective function system and all constraints, a preliminary setting scheme for the daily skylight opening status and skylight duration within the planning period is obtained. Preferably, this step initializes the transportation and skylight parameter class by setting parameters such as particle swarm size, number of iterations, inertia weight, and learning factor; generates an initial particle swarm based on a low-demand day priority strategy, with each particle corresponding to a set of skylight schemes; decodes the particles to obtain the skylight status, duration, and set of skylight days, and obtains feasible skylight schemes through a multi-dimensional constraint repair process.

[0022] Step S230: Calculate and verify the transport capacity based on the preliminary setting scheme and train technical parameters. Calculate the actual daily line throughput capacity and transport volume based on the track window duration, pure train running time, additional time for starting and stopping, additional time for slow travel, idle time before the track window, and train tracking interval. Verify whether it meets the daily freight demand and obtain a track window duration setting scheme that meets the transport capacity guarantee requirements.

[0023] Specifically, this step calculates the daily route capacity and actual transport volume based on the skylight plan to verify whether the freight demand constraints are met.

[0024] Step S300: Based on the construction and maintenance task parameters and the track window duration setting scheme, optimize the construction task scheduling. By constructing and solving the heavy-haul railway construction plan model, obtain the construction task allocation and scheduling scheme, and calculate the construction task completion rate. It should be noted that step S300 constructs a heavy-haul railway construction plan model based on track window duration matching to achieve micro-schedule of lower-level track windows and construction, thereby optimizing the arrangement of construction tasks and efficiently allocating resources within the dynamically changing track window constraint boundaries.

[0025] Further, step S300 includes steps S310 to S330.

[0026] Step S310: Based on the construction and maintenance task parameters and the track window duration setting scheme, a scheduling model is constructed. An objective function is constructed with the comprehensive objectives of maximizing the track window duration matching degree, maximizing the task utility ratio, and maximizing the track window comprehensive utilization rate. The uniqueness of task allocation, the continuity of operation, the number of parallel operations, the number of tasks per day, and the temporal relationship constraints between tasks (sequential, parallel, mutually exclusive, and independent) are integrated to obtain the heavy-haul railway construction plan model. Specifically, the objective function is to maximize a comprehensive objective that includes the average skylight duration matching degree, task utility ratio, and average skylight comprehensive utilization rate within the planning period. The skylight duration matching degree, defined as the ratio of the actual construction time within each day's skylights during the planning period, after task combination, to the skylight duration itself, is close to the actual construction time within the skylights. The task utility ratio assigns higher weight to construction and maintenance projects that need to be completed as early as possible; task combination, allocation, and timing optimization should ensure the completion of key tasks, and important tasks should be allocated to early skylights. The skylight comprehensive utilization rate is the ratio of the average time spent simultaneously performing two or more construction tasks during the planning period to the average daily skylight duration. The specific mathematical expression of the objective function is: ; ; ; ; In the formula, The first time-lattice discretization is used. The total number of time slots for each skylight day, in units of; For the first The duration of a "skylight day" refers to the duration of a decision-making window at the top level, expressed in minutes. The smallest time unit for time discretization, in minutes; Total number of times skylights were opened during the planning period, in units of times; For the first The day of the skylight The occupancy indicator variable for each time cell is 1 if occupied and 0 if not occupied; For the first Weighting of the importance of each construction task; For the number The construction and maintenance tasks were assigned to the first The status of each skylight day is determined by a value of 1 indicating allocation and a value of 0 indicating no allocation. Number the day of the skylight; For the first The day of the skylight The parallel occupancy indicator variable for each time slot. If there are two or more tasks in the time slot at the same time, the value is 1, otherwise it is 0. Optimize the sunroof duration matching target; Optimize the task utility ratio objective; Optimize the overall utilization rate of sunroofs.

[0027] The constraints include constraints on task assignment uniqueness, time grid occupancy validity, task continuity, task allocation, number of parallel jobs, number of tasks per day, temporal coupling, and the definition of auxiliary variables for window utilization. The mathematical expressions for these constraints are: ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; In the formula, , An index for construction tasks; Index for skylight days; For the index of the time cell; For the number The construction and maintenance tasks were assigned to the first The state of a skylight day; This refers to the collection of all construction tasks to be arranged within the planning period; This refers to the collection of all skylight days within the planning period; Indicates task On the day of the skylight The Whether a time cell is in an occupied state; a value of 1 indicates a construction or maintenance task. Currently in operation; a value of 0 indicates a construction or maintenance task. Unoccupied state; For the first The total number of time cells after discretization of each skylight day; For the first The number of time slots required for each construction task; For the first Duration of the construction task; The basic unit of time is duration; Indicates the first The task in the The start time of each skylight day is numbered; Indicates the first The task in the The start time of each skylight day is numbered; Indicates the first The task in the The end time of each skylight day is numbered; For the first The duration of a skylight on a given day; The maximum number of parallel tasks allowed within a single timeframe; The maximum number of tasks that can be scheduled within a single skylight day; and The skylight indicator and intermediate variables represent the construction tasks, respectively. and Whether to be assigned to the same window and task and Should sunroofs be allocated at the same time? In the middle, if it is allocated, it is 1; otherwise, it is 0. This refers to the set of tasks in sequence during the planning period. This refers to the set of parallel task pairs within the planning period. This is the set of mutually exclusive task pairs within the planning period. A set of independent tasks within the planning period; This is a maximum value used in the "Big M method" to linearize logical conditions; and These are the auxiliary variables defined by the Big M method.

[0028] The aforementioned constraint system first establishes basic allocation and time occupancy constraints, requiring that each construction task must be assigned to and can only be executed within a single work window, its operation process must be continuous across discrete time grids, and its start and end times must not exceed the boundaries of its assigned work window. Secondly, resource capacity constraints explicitly limit the upper limit on the number of tasks operating concurrently at the same time and the total number of tasks that can be scheduled within a single work window, to prevent on-site resource overload and organizational chaos. The temporal coupling constraints between tasks precisely model and enforce four core logical relationships that may exist in the construction task network: tasks with a sequential order must begin only after their preceding tasks are completed; tasks defined as parallel must start simultaneously on the same work window; mutually exclusive tasks are prohibited from overlapping operations within the same time grid; and independent tasks must exclusively occupy time resources during their operation period, prohibiting other tasks from performing simultaneously. Furthermore, the model also introduces auxiliary variables and linearization techniques (such as the "Big M method") to transform these complex logical conditions into a form that can be handled by the mathematical programming model. The entire constraint system together constitutes a rigorous decision space, ensuring that the final generated construction plan meets all realistic operational constraints.

[0029] Step S320: Optimize the task-maintenance window allocation based on the heavy-haul railway construction plan model. By searching the solution space of the model to find feasible solutions to allocate each construction task to each maintenance window day, and satisfying the hard constraints that all tasks must be allocated only once and the number of tasks per day does not exceed the upper limit, a preliminary allocation scheme of construction tasks to maintenance windows day is obtained. Step S330: Based on the preliminary allocation plan, the task timing is scheduled within the time window. For each time window day on which a task is assigned, specific start and end times are arranged for each task within the given time window duration. The constraints of task operation duration, continuity, and complex temporal coupling relationships between tasks are satisfied to obtain a construction task allocation and scheduling plan that includes specific operation time arrangements.

[0030] Further, step S330 includes steps S331 to S333.

[0031] Step S331: Discretize the skylight time resources according to the preliminary allocation plan. By dividing the continuous operation time of each skylight day into discrete time grids according to equal time intervals, the time grid sequence of each skylight day is obtained. Specifically, this step uses a time-grid discretization method to divide the continuous skylight duration into equally spaced discrete time units.

[0032] Step S332: Perform temporal constraint mapping based on the time grid sequence. By mapping the four temporal relationships between tasks—sequential, parallel, mutually exclusive, and independent—to the constraints occupied by tasks in different time grids, and linearizing the coupling constraints, a task-time grid occupancy relationship framework with temporal constraints is obtained. Specifically, this step maps the temporal relationships between tasks to time grid occupancy constraints and uses the Big M method to linearize the coupling constraints.

[0033] Step S333: Arrange the start and end times of tasks according to the task-time grid occupancy relationship framework. By scheduling independent tasks, parallel group tasks, critical path tasks and ordinary non-critical tasks in order of constraint rigidity from high to low, and assigning specific start and end positions of time grids to each task, the preliminary arrangement results of the start and end times of all tasks within each day window are obtained. Specifically, this step involves inputting the baseline duration of the skylight, the flexible extension boundary, construction task parameters, and constraint relationships to construct a task information table containing core information such as task duration, weight, temporal dependency, and constraint type. A directed acyclic dependency graph of tasks is then constructed, and task time parameters are calculated through topological sorting to identify critical paths and determine task scheduling priorities. Scheduling is completed in descending order of constraint rigidity, at the levels of independent tasks, parallel task groups, critical path tasks, and ordinary non-critical tasks, prioritizing constraints with high rigidity and high coupling to reduce the probability of subsequent scheduling conflicts. Temporal constraints such as sequence, parallelism, and mutual exclusion, as well as resource constraints such as the number of parallel tasks and the number of tasks per day, are verified. Differentiated strategies are used to resolve scheduling conflicts, and a preliminary feasible solution is output.

[0034] Step S334: Based on the preliminary layout results and the preset flexible extension boundary, the skylight duration is flexibly adjusted. By verifying whether the preliminary layout results of each skylight day can complete all the assigned tasks within the baseline skylight duration, if there are tasks that cannot be completed, the skylight duration of that skylight day is extended within the flexible extension boundary and the task start and end time arrangement is re-executed until a feasible solution is obtained or the upper limit of the flexible extension boundary is reached, thus obtaining the construction task allocation and scheduling scheme.

[0035] Specifically, if the preliminary plan cannot be completed within the baseline window duration, the window duration is extended within the elastic boundary, and steps S333-S334 are re-executed until a feasible plan is obtained or the upper limit of the elastic boundary is reached; a full closed-loop secondary verification of the scheduling plan is performed to repair residual conflicts, and finally the execution sequence plan, construction completion rate and core performance indicators of the tasks within the window are output.

[0036] Furthermore, the calculated construction task completion rate includes: Based on the arrangement results, the task completion status is determined. By verifying whether the job time of each assigned task falls within the duration of its respective window day and satisfies all temporal coupling relationships and the constraints on the number of parallel jobs, it is determined whether the task has been successfully arranged and the arrangement status of each task is obtained. The number of tasks is summarized based on the arrangement status of each task. By summing up the number of tasks that have been successfully arranged in all construction and maintenance tasks, the total number of tasks that have been arranged and executed within the planning period is obtained. The completion rate is calculated based on the total number of tasks that have been assigned. This is done by comparing the total number of tasks with the total number of all construction and maintenance tasks to be carried out during the planning period, thus obtaining a quantified construction task completion rate.

[0037] Furthermore, step S300 also includes steps S340 to S360.

[0038] Step S340: Determine the task completion status according to the construction task allocation and scheduling plan. By verifying whether the operation time of each assigned task falls within the duration of its respective day window and satisfies all temporal coupling relationships and the constraints on the number of parallel operations, determine whether the task has been successfully arranged and obtain the arrangement status of each task. Step S350: Summarize the number of tasks based on the arrangement status of each task. By accumulating the number of tasks that have been successfully arranged in all construction and maintenance tasks, the total number of tasks that have been arranged for execution within the planning period is obtained. Step S360: Calculate the completion rate based on the total number of tasks that have been scheduled for execution. The quantitative construction task completion rate is obtained by comparing the total number of tasks with the total number of all construction and maintenance tasks to be carried out during the planning period.

[0039] Specifically, step S340 performs a feasibility closed-loop verification of the generated construction task allocation and scheduling scheme: by verifying each task assigned to a specific work window, whether the start and end times of its planned operation fall entirely within the total duration of that work window, and whether its execution process satisfies the temporal coupling relationships such as sequence, parallelism, and mutual exclusion among all tasks, as well as the resource constraints on the number of parallel operations, it determines whether each task can be successfully scheduled under the given scheme and outputs the scheduling status of all tasks. Based on this, step S350 summarizes the scheduling status of all construction and maintenance tasks, and by accumulating the number of tasks marked as "successfully scheduled," obtains the total number of tasks that can be actually scheduled and executed within the planning period. Finally, step S360 calculates the completion rate based on this total number of tasks, and by comparing it with the total number of all pending construction and maintenance tasks within the planning period, a quantitative construction task completion rate is finally output.

[0040] Step S400: Perform two-layer collaborative iterative optimization based on the construction task completion rate. By feeding the construction task completion rate back to the track window duration setting model to adjust the track window scheme, the heavy-haul railway construction plan model is driven to re-schedule tasks and recalculate the completion rate. This process is repeated until the target collaboration converges, resulting in the final track window setting and construction plan scheme.

[0041] Understandably, this step achieves collaborative iterative optimization of the two-layer model by feeding back the completion rate of the lower-level construction tasks to the upper-level track window duration setting model. Ultimately, this achieves the collaborative goals of precise guarantee of freight demand, efficient utilization of transport capacity resources, and orderly progress of construction tasks. Preferably, the upper layer uses the PSO algorithm to solve the track window duration setting model that considers the coordination and matching of freight demand, while the lower layer uses the NSGA-II algorithm and a nested time-series optimization algorithm based on the critical path method to solve the heavy-haul railway construction plan model based on track window duration matching. The two layers are strongly coupled through the track window duration parameter. The output of the upper layer serves as the input of the lower layer, and the completion rate of the lower-level construction tasks is fed back to the upper layer, driving the upper-level algorithm to iteratively adjust the track window scheme, ultimately achieving collaborative optimization of freight and construction.

[0042] Furthermore, step S400 also includes steps S410 to S430.

[0043] Step S410: Calculate the overall fitness based on the construction task completion rate. By using the construction task completion rate as a feedback indicator, and combining it with the freight demand matching target and total cost target of the skylight duration setting model, calculate the overall fitness value used to evaluate the merits of the current skylight duration scheme. Step S420: Based on the comprehensive fitness value, iteratively update the sunroof scheme. By selecting a better scheme based on the comprehensive fitness value and driving the sunroof duration setting model to adjust the sunroof duration decision variables, the updated sunroof duration scheme is obtained. Step S430: Based on the preset iteration termination conditions, perform optimization termination judgment and output the results. By judging whether the number of iterations has reached the upper limit or whether the comprehensive fitness value has converged, if the conditions are not met, the updated track window duration scheme is passed to the heavy-haul railway construction plan model to re-execute the task scheduling optimization. If the conditions are met, the iteration is terminated, and the final track window setting scheme and construction plan scheme are obtained.

[0044] Specifically, the algorithm is initialized by setting core parameters including particle swarm size, number of iterations, inertia weight, and learning factor, and inputting various basic parameters related to transportation and sunroof openings. The upper-level sunroof duration setting model is solved using the Particle Swarm Optimization (PSO) algorithm. Its iteration begins with generating an initial particle swarm based on a "low-demand day priority" strategy, where each particle encodes a set of sunroof opening schemes. Subsequently, each particle is decoded to obtain the specific sunroof status, duration, and set of sunroof days. A multi-dimensional constraint repair process ensures the feasibility of the scheme, while daily route capacity is calculated to verify that it meets freight demand constraints.

[0045] Next, the lower-level construction task scheduling phase begins. The upper-level layer passes the window allocation scheme, which includes flexible boundary extension information, to the lower-level layer. The solution for the lower-level heavy-haul railway construction plan model employs a multi-objective genetic algorithm (NSGA-II) nested with a time-series optimization subroutine based on the critical path method (CPM). The lower-level algorithm first sets its own parameters, such as population size, number of iterations, crossover and mutation probabilities, and generates an initial task-window allocation population based on a strategy of topological sorting and parallel group binding. By performing parallel group consistency repair and time-series reversal repair on the individuals in the population, a preliminary allocation scheme that satisfies all hard constraints is output. For each allocation scheme, the inner CPM time-series scheduling algorithm is further invoked. This subroutine takes into account the baseline window duration, the flexible extension boundary, and task parameters, constructs a task information table and a directed acyclic graph of dependencies, and identifies critical paths through topological sorting to determine scheduling priorities. Subsequently, hierarchical scheduling is performed according to the order of constraint rigidity from high to low (independent tasks, parallel group tasks, critical path tasks, and ordinary non-critical tasks), and various timing and resource conflicts are verified and repaired. If the scheme is not feasible within the baseline duration, the window duration is extended within the flexible boundary and rescheduled. Finally, the CPM subroutine outputs the specific execution sequence, construction completion rate, and other multi-objective performance indicators for tasks within each window day. Based on these results, the lower-level NSGA-II algorithm evaluates and selects individuals in the population using feasibility-first non-dominated sorting and crowding distance calculation, and updates the population through binary tournament selection, simulated crossover, mutation, and other operations until the required number of iterations is reached. Finally, a Pareto optimal solution set is output, and the construction completion rate of representative solutions is fed back to the upper layer.

[0046] Afterwards, the process enters the collaborative iteration step. In step S410, the upper layer calculates the comprehensive fitness based on the construction task completion rate: the algorithm uses the construction completion rate fed back from the lower layer as a core feedback indicator, and combines it with the freight demand matching degree target and total cost target optimized by the upper layer's window duration setting model to jointly calculate the particle comprehensive fitness value used to evaluate the merits of the current window scheme. Step S420 iteratively updates the window scheme based on the comprehensive fitness value: the upper layer PSO algorithm updates the individual historical best solution of each particle and the global historical best solution of the entire population based on the calculated fitness value, thereby driving the particles to update their velocity and position, that is, adjust the decision variables of window duration, thereby generating an updated and better window duration setting scheme. Step S430 performs optimization termination judgment and outputs results according to the preset iteration termination conditions: the algorithm judges whether the maximum number of iterations has been reached or the comprehensive fitness value has converged. If the termination condition is not met, the updated window scheme is passed back to the lower layer heavy-haul railway construction plan model to re-execute a new round of task scheduling and completion rate calculation, thereby starting the next iteration cycle. This process repeats until the termination condition is met. Finally, the algorithm terminates its iteration and outputs the optimal track maintenance window setting scheme and the corresponding refined heavy-haul railway construction plan.

[0047] Example 2: like Figure 2 As shown, this embodiment provides a heavy-haul railway construction planning system that considers transport capacity matching. The system includes: The acquisition module 901 is used to acquire parameters such as transportation demand, train technical parameters, track maintenance window setting parameters, track maintenance window opening cost parameters, and construction and maintenance task parameters during the planning period of heavy-haul railways. The adaptation module 902 is used to dynamically adapt the duration of the sunroof based on the transportation demand parameters, train technical parameters, sunroof setting parameters, and sunroof opening cost parameters. By constructing and solving the sunroof duration setting model, the daily dynamically adapted sunroof duration setting scheme within the planning period is obtained. The allocation module 903 is used to optimize the scheduling of construction tasks based on the construction and maintenance task parameters and the track window duration setting scheme. By constructing and solving the heavy-haul railway construction plan model, the construction task allocation and scheduling scheme is obtained, and the construction task completion rate is calculated. The optimization module 904 is used to perform two-layer collaborative iterative optimization based on the construction task completion rate. It adjusts the track window scheme by feeding back the construction task completion rate to the track window duration setting model, and drives the heavy-haul railway construction plan model to re-schedule tasks and recalculate the completion rate. The cycle continues until the target collaboration converges, and the final track window setting and construction plan scheme is obtained.

[0048] In one specific embodiment of this application, the adapter module 902 includes: The first adaptation unit is used to construct a collaborative optimization model based on transportation demand parameters, train technical parameters, sunroof setting parameters, and sunroof opening cost parameters. By constructing an objective function system with the first objective of maximizing the average freight demand matching degree during the planning period and the second objective of minimizing the total cost consisting of fixed sunroof opening cost, duration deviation cost, and unit duration cost, a sunroof duration setting model containing dual optimization objectives is obtained. The second adaptation unit is used to set the model according to the length of the skylight and solve the feasible solution. By searching the solution space of the model to find a feasible solution that simultaneously satisfies the objective function system and all constraints, a preliminary setting scheme for the daily skylight opening status and skylight duration during the planning period is obtained. The third adaptation unit is used to calculate and verify the transport capacity based on the preliminary setting scheme and train technical parameters. It calculates the actual daily line throughput capacity and transport volume based on the track window length, train pure running time, additional start and stop time, and idle time before the track window, verifies whether it meets the daily freight demand, and obtains a track window length setting scheme that meets the transport capacity guarantee requirements.

[0049] In one specific embodiment of this application, the allocation module 903 includes: The first allocation unit is used to construct a scheduling model based on the construction and maintenance task parameters and the track window duration setting scheme. By constructing an objective function with the comprehensive objectives of maximizing the track window duration matching degree, maximizing the task utility ratio, and maximizing the track window comprehensive utilization rate, and integrating the constraints of task allocation uniqueness, operation continuity, number of parallel operations, number of tasks per day, and the temporal relationship constraints between tasks (sequential, parallel, mutually exclusive, and independent), a heavy-haul railway construction plan model is obtained. The second allocation unit is used to optimize the task-maintenance window allocation based on the heavy-haul railway construction plan model. By searching the solution space of the model to find feasible solutions to allocate each construction task to each maintenance window day, and satisfying the hard constraints that all tasks must be allocated only once and the number of tasks per day does not exceed the upper limit, a preliminary allocation scheme of construction tasks to maintenance windows day is obtained. The third allocation unit is used to schedule the tasks within the time window according to the preliminary allocation plan. By arranging specific start and end times for each task within a given time window for each assigned task, and satisfying the constraints of task operation duration, continuity, and complex temporal coupling relationships between tasks, a construction task allocation and scheduling plan containing specific operation time arrangements is obtained.

[0050] In one specific embodiment of this application, the third allocation unit includes: The first allocation subunit is used to discretize the time resources of the skylight according to the preliminary allocation scheme. By dividing the continuous operation time of each skylight day into discrete time grids according to the equal interval time units, the time grid sequence of each skylight day is obtained. The second allocation subunit is used to perform temporal constraint mapping based on the time grid sequence. By mapping the four temporal relationships between tasks—sequential, parallel, mutually exclusive, and independent—to the constraints occupied by tasks in different time grids, and linearizing the coupling constraints, a task-time grid occupancy relationship framework with temporal constraints is obtained. The third allocation subunit is used to arrange the start and end times of tasks according to the task-time grid occupancy relationship framework. By scheduling independent tasks, parallel group tasks, critical path tasks and ordinary non-critical tasks in order of constraint rigidity from high to low, and assigning specific start and end positions of time grids to each task, the preliminary arrangement results of the start and end times of all tasks within each day window are obtained. The fourth allocation subunit is used to flexibly adjust the duration of the skylight based on the preliminary layout results and the preset flexible extension boundary. It verifies whether the preliminary layout results of each skylight day can complete all the allocated tasks within the baseline skylight duration. If there are tasks that cannot be completed, the skylight duration of that skylight day is extended within the flexible extension boundary and the task start and end time arrangement is re-executed until a feasible solution is obtained or the upper limit of the flexible extension boundary is reached, thus obtaining the construction task allocation and scheduling scheme.

[0051] In one specific embodiment of this application, the allocation module 903 further includes: The fourth allocation unit is used to determine the task completion status according to the construction task allocation and scheduling scheme. By verifying whether the operation time of each allocated task falls within the duration of its respective day window and satisfies all temporal coupling relationships and the constraints of the number of parallel operations, it determines whether the task has been successfully arranged and obtains the arrangement status of each task. The fifth allocation unit is used to summarize the number of tasks based on the arrangement status of each task. By accumulating the number of tasks that have been successfully arranged in all construction and maintenance tasks, the total number of tasks that have been arranged for execution within the planning period is obtained. The sixth allocation unit is used to calculate the completion rate based on the total amount of tasks that have been arranged for execution. It obtains the quantitative construction task completion rate by calculating the ratio of the total amount of tasks to the total amount of all construction and maintenance tasks to be carried out during the planning period.

[0052] In one specific embodiment of this application, the optimization module 904 includes: The first optimization unit is used to calculate the comprehensive fitness based on the completion rate of construction tasks. By using the completion rate of construction tasks as a feedback indicator, it combines the freight demand matching degree target and the total cost target of the skylight duration setting model to calculate the comprehensive fitness value used to evaluate the merits of the current skylight duration scheme. The second optimization unit is used to iteratively update the sunroof scheme based on the comprehensive fitness value. It selects a better scheme based on the comprehensive fitness value and drives the sunroof duration setting model to adjust the sunroof duration decision variables, thereby obtaining the updated sunroof duration scheme. The third optimization unit is used to determine the optimization termination and output the results according to the preset iteration termination conditions. It determines whether the number of iterations has reached the upper limit or whether the comprehensive fitness value has converged. If the conditions are not met, the updated track window duration scheme is passed to the heavy-haul railway construction plan model to re-execute the task scheduling optimization. If the conditions are met, the iteration is terminated, and the final track window setting scheme and construction plan scheme are obtained.

[0053] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for compiling a construction plan for heavy-haul railways that considers transport capacity matching, characterized in that, include: Obtain parameters for transportation demand, train technical parameters, track maintenance window settings, track maintenance window opening costs, and construction and maintenance tasks during the planning period of heavy-haul railways; Based on the transportation demand parameters, the train technical parameters, the sunroof setting parameters, and the sunroof opening cost parameters, the sunroof duration is dynamically adapted. By constructing and solving the sunroof duration setting model, a daily dynamically adapted sunroof duration setting scheme is obtained within the planning period. Based on the construction and maintenance task parameters and the track maintenance window duration setting scheme, the construction task scheduling is optimized. By constructing and solving the heavy-haul railway construction plan model, the construction task allocation and scheduling scheme is obtained, and the construction task completion rate is calculated. Based on the completion rate of the construction tasks, a two-layer collaborative iterative optimization is performed. The completion rate of the construction tasks is fed back to the track window duration setting model to adjust the track window scheme, and the heavy-haul railway construction plan model is driven to re-schedule tasks and recalculate the completion rate. This process is repeated until the target collaboration converges, and the final track window setting and construction plan scheme is obtained.

2. The method for compiling a heavy-haul railway construction plan considering transport capacity matching according to claim 1, characterized in that, Based on the transportation demand parameters, the train technical parameters, the sunroof setting parameters, and the sunroof opening cost parameters, the sunroof duration is dynamically adapted, including: Based on the transportation demand parameters, train technical parameters, sunroof setting parameters, and sunroof opening cost parameters, a collaborative optimization model is constructed. By constructing an objective function system with the first objective of maximizing the average freight demand matching degree during the planning period and the second objective of minimizing the total cost consisting of sunroof opening fixed cost, duration deviation cost, and unit duration cost, a sunroof duration setting model containing dual optimization objectives is obtained. Based on the model for setting the skylight duration, feasible solutions are obtained. By searching the solution space of the model for feasible solutions that simultaneously satisfy the objective function system and all constraints, a preliminary setting scheme for the daily skylight opening status and skylight duration within the planning period is obtained. Based on the preliminary setup plan and the train technical parameters, the transport capacity is calculated and verified. Based on the track window duration, train pure running time, start-stop additional time, idle time before the track window, slow travel additional time, and train tracking interval, the actual daily line throughput capacity and transport volume are calculated to verify whether it meets the daily freight demand and obtain a track window duration setting plan that meets the transport capacity guarantee requirements.

3. The method for compiling a heavy-haul railway construction plan considering transport capacity matching according to claim 1, characterized in that, Based on the construction and maintenance task parameters and the skylight duration setting scheme, the construction task scheduling is optimized, including: Based on the construction and maintenance task parameters and the track window duration setting scheme, a scheduling model is constructed. An objective function is constructed with the comprehensive objectives of maximizing the average track window duration matching degree, maximizing the task utility ratio, and maximizing the average track window comprehensive utilization rate during the planning period. The uniqueness of task allocation, the continuity of operation, the number of parallel operations, the number of tasks per day, and the temporal relationship constraints between tasks (sequential, parallel, mutually exclusive, and independent) are integrated to obtain the heavy-haul railway construction plan model. Based on the heavy-haul railway construction plan model, task-maintenance window allocation optimization is performed. By searching the solution space of the model, feasible schemes are searched to allocate each construction task to each maintenance window day, and the hard constraints of all tasks must be allocated only once and the number of tasks per day does not exceed the upper limit are met, a preliminary allocation scheme of construction tasks to maintenance windows day is obtained. Based on the preliminary allocation scheme, the task timing is scheduled within the time window. For each time window day with assigned tasks, specific start and end times are arranged for each task within the given time window duration. This satisfies the constraints of task operation duration, continuity, and complex temporal coupling relationships between tasks, resulting in a construction task allocation and scheduling scheme that includes specific operation time arrangements.

4. The method for compiling a heavy-haul railway construction plan considering transport capacity matching according to claim 3, characterized in that, Based on the preliminary allocation scheme, the task timing within the skylight is scheduled, including: Based on the preliminary allocation scheme, the time resources for the skylight are discretized by dividing the continuous working time of each skylight day into discrete time grids according to equal time intervals, thus obtaining the time grid sequence for each skylight day. Based on the time grid sequence, a time constraint mapping is performed. By mapping the four time relationships between tasks—sequential, parallel, mutually exclusive, and independent—to the constraints occupied by tasks in different time grids, and linearizing the coupling constraints, a task-time grid occupancy relationship framework with time constraints is obtained. Based on the task-time grid occupancy framework, the start and end times of tasks are arranged. Independent tasks, parallel group tasks, critical path tasks and ordinary non-critical tasks are scheduled in order of constraint rigidity from high to low. Each task is assigned a specific start and end position of the time grid, and the preliminary arrangement of the start and end times of all tasks within each day window is obtained. Based on the preliminary arrangement results and the preset flexible extension boundary, the duration of the skylight is flexibly adjusted. By verifying whether the preliminary arrangement results for each skylight day can complete all the assigned tasks within the baseline skylight duration, if there are tasks that cannot be completed, the skylight duration for that skylight day is extended within the flexible extension boundary, and the task start and end time arrangement is re-executed until a feasible solution is obtained or the upper limit of the flexible extension boundary is reached, thus obtaining the construction task allocation and scheduling scheme.

5. The method for compiling a heavy-haul railway construction plan considering transport capacity matching according to claim 3, characterized in that, The calculation of the construction task completion rate includes: The task completion status is determined according to the construction task allocation and scheduling scheme. By verifying whether the operation time of each assigned task falls within the duration of its respective day window and satisfies all temporal coupling relationships and the constraints on the number of parallel operations, it is determined whether the task has been successfully arranged and the arrangement status of each task is obtained. The number of tasks is summarized based on the arrangement status of each task. By summing up the number of tasks that have been successfully arranged in all construction and maintenance tasks, the total number of tasks that have been arranged for execution within the planning period is obtained. The completion rate is calculated based on the total number of tasks that have been scheduled for execution. This is done by comparing the total number of tasks with the total number of all construction and maintenance tasks to be carried out during the planning period, thus obtaining a quantified construction task completion rate.

6. A construction planning system for heavy-haul railways that considers transport capacity matching, characterized in that, include: The acquisition module is used to acquire transportation demand parameters, train technical parameters, track maintenance window setting parameters, track maintenance window opening cost parameters, and construction and maintenance task parameters during the planning period of heavy-haul railways. The adaptation module is used to dynamically adapt the duration of the sunroof based on the transportation demand parameters, the train technical parameters, the sunroof setting parameters, and the sunroof opening cost parameters. By constructing and solving the sunroof duration setting model, the daily dynamically adapted sunroof duration setting scheme within the planning period is obtained. The allocation module is used to optimize the scheduling of construction tasks based on the construction and maintenance task parameters and the track maintenance window duration setting scheme. By constructing and solving the heavy-haul railway construction plan model, the construction task allocation and scheduling scheme is obtained, and the construction task completion rate is calculated. The optimization module is used to perform two-layer collaborative iterative optimization based on the completion rate of the construction tasks. It adjusts the track window scheme by feeding back the completion rate of the construction tasks to the track window duration setting model, and drives the heavy-haul railway construction plan model to re-schedule tasks and recalculate the completion rate. The cycle continues until the target collaboration converges, and the final track window setting and construction plan scheme is obtained.

7. The heavy-haul railway construction planning system considering transport capacity matching according to claim 6, characterized in that, The adaptation module includes: The first adaptation unit is used to construct a collaborative optimization model based on the transportation demand parameters, the train technical parameters, the sunroof setting parameters, and the sunroof opening cost parameters. By constructing an objective function system with the first objective of maximizing the average freight demand matching degree during the planning period and the second objective of minimizing the total cost consisting of the fixed cost of sunroof opening, the duration deviation cost, and the unit duration cost, a sunroof duration setting model containing dual optimization objectives is obtained. The second adaptation unit is used to set the model according to the skylight duration and solve the feasible solution. By searching the solution space of the model to find a feasible solution that simultaneously satisfies the objective function system and all constraints, a preliminary setting scheme for the daily skylight opening status and skylight duration within the planning period is obtained. The third adaptation unit is used to calculate and verify the transport capacity based on the preliminary setting scheme and the train technical parameters. Based on the track window duration, train pure running time, start-stop additional time, idle time before the track window, slow travel additional time and train tracking interval, it calculates the actual daily line throughput capacity and transport volume, verifies whether it meets the daily freight demand, and obtains a track window duration setting scheme that meets the transport capacity guarantee requirements.

8. The heavy-haul railway construction planning system considering transport capacity matching according to claim 6, characterized in that, The allocation module includes: The first allocation unit is used to construct a scheduling model based on the construction and maintenance task parameters and the track window duration setting scheme. It constructs an objective function with the comprehensive objectives of maximizing the average track window duration matching degree, maximizing the task utility ratio, and maximizing the average track window comprehensive utilization rate during the planning period. It also integrates the constraints of task allocation uniqueness, operation continuity, number of parallel operations, number of tasks per day, and the temporal relationship between tasks (sequential, parallel, mutually exclusive, and independent) to obtain a heavy-haul railway construction plan model. The second allocation unit is used to optimize the task-maintenance window allocation according to the heavy-haul railway construction plan model. By searching the solution space of the model to allocate each construction task to each maintenance window day, and satisfying the hard constraints that all tasks must be allocated only once and the number of tasks per day does not exceed the upper limit, a preliminary allocation scheme of construction tasks to maintenance windows day is obtained. The third allocation unit is used to schedule the task sequence within the skylight according to the preliminary allocation scheme. By arranging specific start and end times for each task within a given skylight duration for each assigned task day, and satisfying the constraints of task operation duration, continuity, and complex temporal coupling relationships between tasks, a construction task allocation and scheduling scheme containing specific operation time arrangements is obtained.

9. The heavy-haul railway construction planning system considering transport capacity matching according to claim 8, characterized in that, The third allocation unit includes: The first allocation subunit is used to discretize the skylight time resources according to the preliminary allocation scheme. By dividing the continuous operation time of each skylight day into discrete time grids according to equal interval time units, the time grid sequence of each skylight day is obtained. The second allocation subunit is used to perform temporal constraint mapping based on the time grid sequence. By mapping the four temporal relationships between tasks—sequential, parallel, mutually exclusive, and independent—to the constraints occupied by tasks in different time grids, and linearizing the coupling constraints, a task-time grid occupancy relationship framework with temporal constraints is obtained. The third allocation subunit is used to arrange the start and end times of tasks according to the task-time grid occupancy relationship framework. By scheduling independent tasks, parallel group tasks, critical path tasks and ordinary non-critical tasks in order of constraint rigidity from high to low, and assigning specific start and end positions of time grids to each task, the preliminary arrangement results of the start and end times of all tasks within each day window are obtained. The fourth allocation subunit is used to flexibly adjust the skylight duration based on the preliminary arrangement results and the preset flexible extension boundary. It verifies whether the preliminary arrangement results for each skylight day can complete all the allocated tasks within the baseline skylight duration. If there are tasks that cannot be completed, the skylight duration for that skylight day is extended within the flexible extension boundary, and the task start and end time arrangement is re-executed until a feasible solution is obtained or the upper limit of the flexible extension boundary is reached, thus obtaining a construction task allocation and scheduling scheme.

10. The heavy-haul railway construction planning system considering transport capacity matching according to claim 8, characterized in that, The allocation module further includes: The fourth allocation unit is used to determine the task completion status according to the construction task allocation and scheduling scheme. By verifying whether the operation time of each allocated task falls within the duration of its respective day window and satisfies all temporal coupling relationships and the constraints of the number of parallel operations, it determines whether the task has been successfully arranged and obtains the arrangement status of each task. The fifth allocation unit is used to summarize the number of tasks according to the arrangement status of each task. By accumulating the number of tasks that have been successfully arranged in all construction and maintenance tasks, the total number of tasks that have been arranged for execution within the planning period is obtained. The sixth allocation unit is used to calculate the completion rate based on the total number of tasks that have been scheduled for execution. The quantitative construction task completion rate is obtained by calculating the ratio of the total number of tasks to the total number of all construction and maintenance tasks to be carried out during the planning period.

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