An energy-saving scheduling method and system for an automated dry bulk cargo terminal considering dynamic switching of belt conveyors

Abstract

An energy-saving scheduling method and system for automated dry bulk cargo terminals, considering dynamic switching of conveyor belts, belongs to the field of port operation scheduling. First, it collects characteristic data of the automated dry bulk cargo terminal's yard, information on stacking and reclaiming operations, and parameter information of the operating equipment. Second, it constructs an energy-saving scheduling optimization model for the automated dry bulk cargo terminal considering dynamic switching of conveyor belts. Finally, it solves the energy-saving scheduling optimization model and generates an operation scheduling plan for the planning period based on the solution results. This invention enables integrated optimization of yard location allocation, task transportation route selection, and conveyor belt start-up and shutdown sequence in multi-task, multi-transportation-path scenarios; effectively reducing idle operation and repeated start-ups and shutdowns, significantly reducing operational energy consumption while ensuring operational efficiency, and providing technical support for the intelligent collaboration and green, efficient operation of dry bulk cargo terminals.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of port operation scheduling, and relates to an energy-saving scheduling method and system for an automated bulk cargo terminal considering dynamic switching of belt conveyors. BACKGROUND

[0002] Bulk dry bulk cargoes such as coal and ore are important energy and industrial raw materials, and are the cornerstone of social and economic development. An automated bulk cargo terminal is configured with an all-electric, continuous operation system composed of belt conveyors, stackers, reclaimer, car dumper, and ship loader. A plurality of piles are arranged in the yard, each pile is divided into a plurality of discrete stacks along the length direction, and the cargoes occupy a plurality of continuous stacks in the form of stacks. The belt conveyor is the main horizontal transportation equipment for cargoes, and a plurality of belt conveyors are connected by transfer towers to form a belt conveyor system, realizing continuous and efficient transportation of dry bulk cargoes. However, the belt conveyor system has high running power, long lines, and complex start-stop process, and a large amount of idling and repeated start-stop often occurs due to process switching. According to statistics, the energy consumption of the belt conveyor system accounts for about 80% of the total production energy consumption. The existing traditional process of starting and stopping in the order of "downstream first and upstream second" and the operation scheduling method mainly based on human experience, as shown in FIG. 1, are difficult to coordinate the switching of the belt conveyor between different operation processes in time, resulting in long-time idling of the belt conveyor, causing waste of energy and increasing the delay time of the operation. Figure 1

[0003] To solve the above problems, Chinese Invention Patent CN102951428B proposes an energy-saving control method for a belt conveyor system, which realizes automatic speed control by setting a material detection device, a frequency converter, and a programmable controller on the belt conveyor, thereby shortening the idling time of the belt conveyor in a single transportation process and improving the transportation efficiency. However, this patent mainly targets one or a few series systems, and matches the cargo flow state through local speed adjustment, without involving the complex scenario of a plurality of transportation lines and a large number of operation processes in parallel, and without considering the energy-saving scheduling method for the integration of production operation plan and equipment control. In terms of group control and start-stop sequence control, Chinese Invention Patent CN110844517B proposes a belt conveyor arbitrary sequence start-stop control system and control method, which realizes automatic calculation and control of the start-stop sequence and start mode of the belt conveyor through an intelligent monitoring platform, ring network interaction master control device, and multiple controllers, thereby reducing the energy consumption of the belt conveyor in idling operation. This patent proves the feasibility of arbitrary sequence start-stop control and group control of the belt conveyor, but still mainly stays at the start-stop logic and control scheme at the equipment level, without considering the conflict relationship and switching process between different operation processes, especially in the demand and constraints of the overall operation plan.

[0004] ​In summary, how to provide an energy-saving scheduling method and system for automated dry bulk cargo terminals that considers the dynamic switching of belt conveyors, and achieve integrated optimization of task scheduling and belt conveyor start-stop control, thereby reducing belt conveyor energy consumption while maintaining operational efficiency, has become an urgent problem to be solved. Summary of the Invention

[0005] This invention addresses the problems of high energy consumption of belt conveyor operations, severe idle time during the switching of different work processes, and easy delays in traditional automated dry bulk terminals. In particular, it lacks technical means to coordinate and optimize task scheduling, belt conveyor start-stop control, and dynamic switching processes under the constraints of the overall work plan. This invention provides an energy-saving scheduling method and system for automated dry bulk terminals that considers the dynamic switching of belt conveyors. It can optimize the task-belt conveyor path selection and belt conveyor start-stop control in an integrated manner based on the overall allocation of yard locations and the organization of work processes, so as to achieve energy-saving scheduling that balances terminal operation efficiency and belt conveyor operating energy consumption.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] An energy-saving scheduling method for automated dry bulk cargo terminals considering dynamic switching of belt conveyors, the method comprising the following steps:

[0008] S1: Collects characteristic data of automated dry bulk terminal yards, stacking operation task information, reclaiming operation task information, and operating equipment parameter information, as detailed below:

[0009] The automated dry bulk terminal yard characteristic data mainly includes: the number, spatial location and layout of each conveyor belt and transfer tower; the number, number, spatial location, length and storage capacity of each stockpile; the number and number of stacking positions along the length of each stockpile; the set of optional cargo transportation routes between each stockpile and the tippler; the set of optional cargo transportation routes between each stockpile and the ship loader; and the sequence and number of conveyor belts included in each transportation route.

[0010] The stacking task information and retrieving task information data are used to characterize the transportation process of goods between different work points. The stacking task represents the process of transporting goods from the tipper to the yard for stacking, and the retrieving task represents the process of transporting goods from the yard for stacking to the ship loader. It mainly includes: the number of tasks, the number of tasks and the task type (used to distinguish between stacking tasks and retrieving tasks); the amount of goods transported for each task, the number of stacking positions occupied, the earliest time that the operation can start, and the priority of different tasks.

[0011] The parameter information data of the operating equipment mainly includes: the number, start and end connection position, length, rated conveying capacity, rated operating speed, rated power, start-up time and energy consumption, shutdown time, and the time required for a unit quantity of goods to be transported along the belt conveyor; the equipment number and rated operating capacity of stacker, reclaimer, tipper, and ship loader.

[0012] S2: Based on the yard characteristic data, stockpiling task information, reclaiming task information and operating equipment parameter information obtained in S1, construct an energy-saving scheduling optimization model for automated dry bulk cargo terminals that considers the dynamic switching of belt conveyors.

[0013] First, an objective function is established with the goal of minimizing the weighted sum of task completion time and overall energy consumption of the conveyor belt. Then, constraints are constructed from several aspects, including yard space allocation, task transportation route selection, conveyor belt operation sequence and time continuity, and energy consumption of conveyor belt start-up, shutdown and dynamic switching, thus forming an energy-saving scheduling optimization model for automated dry bulk terminal that considers the dynamic switching of conveyor belts.

[0014] S2-1: First, an objective function is constructed for the energy-saving scheduling optimization model of an automated dry bulk terminal that considers the dynamic switching of belt conveyors. This objective function is used to comprehensively measure the time and energy costs of the automated dry bulk terminal under a given yard layout and operation plan. Specifically, the optimization objective is to minimize the weighted sum of the operation time of all tasks from the earliest start time to the actual completion time, and the energy consumption required by the belt conveyor to complete all tasks. To this end, the calculation relationship of task completion time and the calculation relationship of energy consumption of tasks on the belt conveyor are given respectively, and the objective function is established accordingly, as shown in formulas (1) to (3).

[0015] (1)

[0016] (2)

[0017] (3)

[0018] In the formula, Represents a set of tasks. Indicates the task number; Weighting coefficients representing task completion time; Indicates task Completion time; Indicates task The earliest start time of the work; The weighting coefficient representing the energy consumption of the belt conveyor; Indicates a collection of belt conveyors. Indicates the belt conveyor number; Indicates completion of task belt conveyor Energy consumption; Indicates task The start date of the project; Indicates task The time required to operate on the stacker or reclaimer; Indicates the route through which a unit quantity of goods passes. Required shipping time; Represents an infinitely large constant; Represents the set of transportation routes. Indicates the transport route number; It is a 0-1 variable, representing the state when the task... Select transportation route The value is 1 if it is 1, otherwise it is 0. Indicates belt conveyor The rated power, i.e., the energy consumption required to operate per unit time; These respectively represent the completion of the task. Rear belt conveyor End of runtime; Indicates belt conveyor Shutdown time; For 0-1 decision variables, when the task is completed Rear belt conveyor The value is 1 if an immediate shutdown operation is performed, and 0 otherwise. Indicates completion of task belt conveyor Startup time; Indicates belt conveyor Booting time; A variable of 0-1, representing the state during task execution. Front belt conveyor If the power-on process has been completed, the value is 1; otherwise, it is 0. It is also a 0-1 variable, representing the state when the task... On the belt conveyor If the transport is carried out, the value is 1; otherwise, it is 0.

[0019] The objective function is shown in Equation (1), which represents minimizing the total time from the earliest possible start time to the actual completion time of all tasks and the weighted sum of the total energy consumption generated by all belt conveyors to complete each task; the task completion time is calculated as shown in Equation (2), which means that when a task is assigned to a certain transportation path, the completion time of the task is no earlier than the sum of its start time, the working time on the stacker or reclaimer, and the transportation time along the selected path through all belt conveyors. When the task does not select the path, the inequality is relaxed by an infinite constant; the belt conveyor energy consumption is shown in Equation (3), which means that when a task is executed on a certain belt conveyor, the lower bound of the energy consumption of the belt conveyor during the service of the task is determined by its effective running time and rated power. The effective running time is corrected by considering whether to perform shutdown and start-up operations. When the task is not executed on the belt conveyor, the inequality is relaxed by an infinite constant.

[0020] S2-2: Based on the objective function and the relationship between task completion time and belt conveyor energy consumption given in S2-1, further establish relevant constraints on the allocation of stockpile space location to characterize the spatial distribution of stacks on each material pile and its stack position. The relevant constraints on the allocation of stockpile space location include: each stack must and can only be allocated to a certain set of consecutive stack positions on a material pile, as shown in formula (4); the space occupied by the stack in the material pile cannot exceed the length boundary of the material pile, as shown in formula (5); formula (6) then logically constrains the spatial and temporal relationships between any two stacks on the same material pile to avoid spatial and temporal overlap at the same location; under the premise of a given spatial order, it forces the reservation of at least one stack position space interval between different stacks, as shown in formula (7); the stacking task and the picking task corresponding to the same stack satisfy the temporal order relationship, that is, the start time of the picking task is not earlier than the sum of the transportation time required for the completion time of the corresponding stacking task, as shown in formula (8).

[0021] (4)

[0022] (5)

[0023] (6)

[0024] (7)

[0025] (8)

[0026] In the formula, Indicates a collection of material piles. Indicates the stockpile number; Indicates a stockpile The stacking station set on the top, Indicates the stack location number; It is a 0-1 variable, indicating if stacking Assigned to stockpile And the leftmost stack position it occupies is The value is 1 if it is 1, otherwise it is 0. Represents a stacked set. and Indicates the number of different stockpiles; Indicates stacking The length occupied on the material pile Indicates a stockpile Length; and These are 0-1 variables, representing the values ​​in the stockpile. Stacking and Spatial relationships and temporal sequence: when in the stockpile superior The occupied stack space is When on the left It is 1 if it is not 0 otherwise; when in the stockpile superior Prior to When occupying stack space It is 1 if it is 1, otherwise it is 0; similarly, and It is also a 0-1 variable: when in the stockpile superior The occupied stack space is When on the left It is 1 if it is not 0 otherwise; when in the stockpile superior Prior to When occupying stack space It is 1 if it is true, otherwise it is 0; It is a 0-1 variable, indicating if stacking Assigned to stockpile And the leftmost stack position it occupies is The value is 1 if it is 1, otherwise it is 0. and Representing tasks and The start time of the work; Indicates task Operating time on stacker or reclaimer; Represents the set of transportation routes. Indicates the transport route number; Indicates the route taken by a unit quantity of goods. Required shipping time; and These represent the sets of material stacking tasks and material retrieving tasks, respectively; they are tasks. a subset of and Indicates the task number; It is a 0-1 variable, when the task Select transportation route The value is 1 if the condition is met, and 0 otherwise. A 0-1 variable used to represent the state of the material pile. Above and Retrieving Tasks Corresponding stacks and stockpiling tasks Corresponding stacks The order of time between them, when related to the stockpiling task Corresponding stacks Prior to the material collection task in terms of time. Corresponding stacks Complete the task, that is, when the same stack of materials is stacked first and then retrieved. It is 1 if it is true, otherwise it is 0.

[0027] S2-3: Based on the above objective function and the space allocation constraints of the stockyard, establish relevant constraints for task transportation path selection. The relevant constraints for task transportation path selection include: each task must and can only select one transportation path, as shown in formula (9); stockpiling tasks can only select stockpiling paths, and retrieving tasks can only select retrieving paths, so as to ensure the consistency between task type and path attributes, as shown in formula (10); when a certain transportation path cannot reach the stockpile where the corresponding stack of the task is located, the task is prohibited from selecting that transportation path, so as to ensure that the transportation path selected by the task is consistent with the accessibility of the stockyard space layout, as shown in formula (11).

[0028] (9)

[0029] (10)

[0030] (11)

[0031] In the formula, Represents a set of tasks. and These represent the sets of material stacking tasks and material retrieving tasks, respectively. a subset of Indicates the task number; Represents the set of transportation routes. and The sets representing the stacking paths and the retrieving paths are sets. a subset of Indicates the transport route number; It is a 0-1 variable, representing the state when the task... Select transportation route The value is 1 if the condition is met, and 0 otherwise. It is an infinitely large constant; Indicates a collection of material piles. Indicates the stockpile number; Indicates a stockpile The stacking station set on the top, Indicates the stack location number; It is a 0-1 variable, representing the task. Corresponding stacks If assigned to the stockpile And the leftmost stack position it occupies is The value is 1 if it is 1, otherwise it is 0. For path reachability parameters, if the transportation path Able to reach the stockpile If the value is 1, then the value is 1; otherwise, the value is 0.

[0032] S2-4: Based on the task transportation path selection constraints given in S2-3, establish the belt conveyor operation sequence and time continuity related constraints to characterize the allocation relationship and time evolution process of each task in the belt conveyor system. The belt conveyor operation scheduling plan related constraints include: using formula (12) to associate the selection of the task on the transportation path with its allocation relationship on each belt conveyor, ensuring that when a task selects a transportation path containing a certain belt conveyor, the corresponding belt conveyor on the selected path is selected; using formulas (13) to (16) to introduce the starting virtual task and the ending virtual task on each belt conveyor, constraining the connection relationship between the real tasks on the belt conveyor, ensuring the operation sequence of tasks on the same belt conveyor; the task start operation time is the task start transportation time on the selected path, such as As shown in formula (17); the start time of the task cannot be earlier than the earliest time when the task can start, nor earlier than the start-up completion time of the first conveyor belt on the selected path of the task, as expressed by formulas (18) and (19) respectively; the task can only start transportation on the conveyor belt after the conveyor belt has started, as expressed by formula (20); the task must ensure the continuity of cargo flow between conveyor belts on the same transportation path, that is, between adjacent conveyor belts, the time when the cargo leaves the previous conveyor belt is the time when it enters the next conveyor belt, as expressed by formula (21), the specific formula is as follows:

[0033] (12)

[0034] (13)

[0035] (14)

[0036] (15)

[0037] (16)

[0038] (17)

[0039] (18)

[0040] (19)

[0041] (20)

[0042] (twenty one)

[0043] In the formula, It is a 0-1 variable, representing the state when the task... On the belt conveyor If transportation is carried out, the value is 1; otherwise, it is 0. It is also a 0-1 variable, representing the state when the task... Select transportation route The value is 1 if the condition is met, and 0 otherwise. Represents a set of tasks. Indicates the task number; Represents the set of transportation routes. Indicates the transport route number; Indicates a collection of belt conveyors. Indicates the transportation route The collection of belt conveyors on the top is a subset of Indicates the belt conveyor number; This represents an extended task set, consisting of a real task set and a virtual termination task. composition; It is a 0-1 variable, representing the value on the belt conveyor. Virtual Startup Task Then came the actual transportation mission. The value is 1 if the condition is met, and 0 otherwise, indicating a real task. It is a belt conveyor The first transport object on the road; This represents an extended task set, consisting of the real task set and the virtual starting task. composition; It is a 0-1 variable, representing the belt conveyor. On real missions Then came the virtual transportation mission. The value is 1 if the condition is met, and 0 otherwise, indicating a real task. It is a belt conveyor The last transported item on the route; and All are 0-1 decision variables, used to represent the decision variables in the belt conveyor. On the task With the task The order of adjacent items in a belt conveyor On the task Immediately following the mission During execution, The value is 1 if it is not 1, and 0 otherwise; when on the belt conveyor On the task Immediately following the mission During execution, The value is 1 if it is set to 1, otherwise the value is 0. Indicates the start time of the task; Indicates task In the transportation route The start time of transport on the first conveyor belt; It is an infinitely large constant; Indicates task The earliest start time of the work; Indicates to perform a task Transportation routes The start-up time of the first belt conveyor; Indicates the transportation route The time required for the first belt conveyor to complete the startup operation; Indicates task On the belt conveyor The start time of transportation; Indicates to perform a task belt conveyor The boot time; Indicates belt conveyor Booting time; A 0-1 variable, representing the belt conveyor. In transportation mission Whether the boot process was previously executed; the value is 1 if the boot process is required, and 0 otherwise. and Representing tasks In its selected transportation route Upper Article and Section The start time of transport on the belt conveyor; Representing a path Upper The time required for a single belt conveyor to transport a unit quantity of goods; Indicates the transportation route The number of belt conveyors included.

[0044] S2-5: Based on the constraints related to the operation sequence and time continuity of the belt conveyor given in S2-4, establish the logical constraints related to the start-up, shutdown, and dynamic switching of the belt conveyor to characterize the start-up and shutdown operations of the belt conveyor between adjacent tasks. These logical constraints related to the start-up, shutdown, and dynamic switching of the belt conveyor uniformly describe the time evolution process of the belt conveyor under both continuous operation and shutdown / restart conditions by providing the downtime of the belt conveyor after completing a task, the start-up and shutdown connection relationship between adjacent tasks, and the start-up and shutdown state conditions of the first and last tasks. Specifically, formula (22) provides a lower bound for the downtime of the conveyor belt after completing the task, based on the start time, unit transport time, and shutdown duration of the task on the conveyor belt, provided that the task has been assigned to a certain conveyor belt. Formulas (23) and (24) link the start time of the next task with the downtime of the previous task under the constraint of the task sequence variable, so that the restart time window of the conveyor belt between two tasks is consistent with whether or not a shutdown operation is performed. Formulas (25) and (26) use virtual start tasks and virtual end tasks to constrain the start-up state of the first task executed by the conveyor belt and the shutdown state of the last task executed with the work sequence. Formula (27) constrains the shutdown decision of the previous task with the start-up state variable of the next task between any pair of adjacent tasks, so that the two dynamic switching modes of continuous operation and shutdown restart remain consistent at the variable level. Specifically, as shown in formulas (22) to (27):

[0045] (twenty two)

[0046] (twenty three)

[0047] (twenty four)

[0048] (25)

[0049] (26)

[0050] (27)

[0051] In the formula, Indicates belt conveyor After completing the task The subsequent downtime; Indicates task On the belt conveyor The start time of transportation; Indicates the route through which a unit quantity of goods passes. Required shipping time; Indicates belt conveyor Shutdown time; For 0-1 decision variables, when the task is completed Rear belt conveyor The value is 1 if an immediate shutdown operation is performed, and 0 otherwise. It is a 0-1 variable, representing the state when the task... On the belt conveyor If transportation is carried out, the value is 1; otherwise, it is 0. Indicates completion of task belt conveyor Startup time; For 0-1 decision variables, when on a belt conveyor On the task Immediately following the mission During execution, The value is 1 if it is set to 1, otherwise the value is 0. A variable of 0-1, representing the state during task execution. Front belt conveyor If the power-on process has been completed, the value is 1; otherwise, it is 0. It is a 0-1 variable, representing the value on the belt conveyor. Virtual Startup Task Then came the actual transportation mission. The value is 1 if the condition is met, and 0 otherwise, indicating a real task. It is a belt conveyor The first transport object on the road; It is a 0-1 variable, representing the belt conveyor. On real missions Then came the virtual transportation mission. The value is 1 if the condition is met, and 0 otherwise, indicating a real task. It is a belt conveyor The last transported item on the route; A variable of 0-1, representing the state during task execution. Front belt conveyor If the power-on process has been completed, the value is 1; otherwise, it is 0.

[0052] S2-6: Based on the constraints related to the operation sequence and time continuity of the belt conveyor given in S2-4 and the constraints related to the start-up and dynamic switching logic of the belt conveyor given in S2-5, constraints related to the number of belt conveyors started and power limits are established to avoid the power load exceeding the upper limit due to too many belt conveyors starting at the same time. The constraints are formed by discretely marking the belt conveyor start-up period and linking the start-up time interval of the belt conveyor under each task with the start-up state variable: Formula (28) limits the number of belt conveyors in the start-up state at any time to not exceed the capacity limit; Formula (29) requires that the start-up process of the task on the belt conveyor covers the start-up time in time, provided that the task has been assigned to a certain belt conveyor; Formulas (30) and (31) link the start-up state variable with the start-up time interval of the belt conveyor, so that when a certain time is marked as "starting", the time must fall between the start-up time and start-up end time of the corresponding task's belt conveyor, thereby characterizing the continuity of the belt conveyor start-up process in the discrete time dimension. Specifically, as shown in Formulas (28) to (31):

[0053] (28)

[0054] (29)

[0055] (30)

[0056] (31)

[0057] In the formula, Represents the set of discrete time steps. Indicates the number of the discrete time step; It is a 0-1 variable, when the task is completed. Transportation, belt conveyor At any moment The value is 1 if the process is in the startup phase, and 0 otherwise. This represents the upper limit of the number of belt conveyors allowed to be in the start-up state at any discrete moment, reflecting the capacity constraint of the terminal power system on the start-up load of belt conveyors; Indicates belt conveyor The boot time is an infinite constant. It is a 0-1 variable, representing the state when the task... On the belt conveyor If transportation is carried out, the value is 1; otherwise, it is 0. Indicates to perform a task belt conveyor The moment the computer boots up.

[0058] S2-7: Based on the objective function and various constraints given in S2-1 to S2-6, further establish the domain constraints of the model variables to ensure the completeness of the above mathematical model definition. Limit the range of values ​​for energy consumption variables, time variables and various 0-1 decision variables respectively, thereby forming a complete variable space for the mixed integer programming model. The domain constraints of the model variables include: limiting the non-negativity of the belt conveyor energy consumption variable through formula (32), limiting the values ​​of various time-related variables to the given time set through formula (33), and declaring the 0-1 discrete characteristics of variables such as path selection, belt conveyor allocation, yard location allocation, stacking relationship, task sequence, start-stop logic and start-up status through formulas (34) to (36). Specifically, as shown in formulas (32) to (36):

[0059] (32)

[0060] (33)

[0061] (34)

[0062] (35)

[0063] (36)

[0064] In the formula, Indicates completion of task belt conveyor Energy consumption; Represents a set of tasks. and Indicates the task number; Indicates a collection of belt conveyors. Indicates the belt conveyor number; Indicates task Completion time; Indicates task On the belt conveyor The start time of transportation; Indicates to perform a task belt conveyor The boot time; Indicates belt conveyor After completing the task The subsequent downtime; Represents the set of transportation routes. Indicates the transport route number; Represents the set of discrete time steps. Indicates the number of the discrete time step; It is a 0-1 variable: Indicates when the task Select transportation route The value is 1 if it is 1, otherwise it is 0. Indicates when the task On the belt conveyor If transportation is carried out, the value is 1; otherwise, it is 0. Indicates when the task is completed Rear belt conveyor The value is 1 if an immediate shutdown operation is performed, and 0 otherwise. Indicates performing a task Front belt conveyor If the power-on process has been completed, the value is 1; otherwise, it is 0. Indicates when completing the task Transportation, belt conveyor At any moment The value is 1 if the process is in the startup phase, and 0 otherwise. Represents a stacked set. and Indicates the number of different stockpiles; Indicates a collection of material piles. Indicates the stockpile number; Indicates a stockpile The stacking station set on the top, Indicates the stack location number; It is a 0-1 variable: , indicating if stacking Assigned to stockpile And the leftmost stack position it occupies is The value is 1 if it is 1, otherwise it is 0. Indicates in the stockpile superior The occupied stack space is The value is 1 if it is on the left, otherwise it is 0; Indicates when in the stockpile superior Prior to The value is 1 when a stack space is occupied, and 0 otherwise. Represents the set of all extended tasks. , and These represent the virtual start task and the virtual end task, respectively. For 0-1 decision variables, when on a belt conveyor On the task Immediately following the mission During execution, The value is 1 if it is not 1, otherwise the value is 0.

[0065] In S2, this invention constructs an energy-saving scheduling optimization model for automated dry bulk cargo terminals, considering the yard structure, conveyor operation process, and task characteristics, taking into account the dynamic switching of conveyors. The model aims to minimize the weighted sum of the total task operation time and the total energy consumption of the conveyors. It utilizes the calculation relationship between task completion time and conveyor energy consumption given in S2-1, the yard spatial location allocation constraints and task transportation path selection constraints established in S2-2 and S2-3, the task operation sequence constraints on the conveyors described in S2-4 and S2-5, the temporal continuity constraints, and the start-stop and dynamic switching logic constraints of the conveyors between adjacent tasks, the terminal power load capacity upper limit constraint in S2-6, and the domain of variables in S2-7. This forms a mixed-integer programming model that integrates yard location allocation, task transportation path selection, and conveyor start-stop control, providing a mathematical foundation for subsequent intelligent solution and generation of energy-saving scheduling schemes.

[0066] S3: Solve the energy-saving scheduling optimization model constructed in S2, and generate the job scheduling scheme within the planning period based on the solution results.

[0067] Based on the yard characteristic data, stockpiling and reclaiming task information, and operating equipment parameter information collected in S1, and combined with the objective function and constraints in S2, the energy-saving scheduling model of the automated dry bulk terminal considering the dynamic switching of belt conveyors is solved. The model instantiation and preparation, hybrid intelligent solution, and scheduling result output are completed in sequence. First, the energy-saving scheduling model is instantiated and prepared for solution. The input data in S1 is mapped to the set, parameters, and initial decision space in the model, and a mixed integer programming framework consisting of the scheduling objective function and constraints is constructed. Then, a hybrid intelligent solution strategy combining the improved genetic algorithm (IGA) and mixed integer programming (MIP) is adopted to jointly optimize the yard location allocation, task transportation path selection, and belt conveyor start-up and shutdown timing, obtaining the optimal or near-optimal solution results that satisfy the constraints. Finally, based on the solution results, the storage location of each stockpiling task in the yard, the start time of each task, the transportation path selection, and the start-up and shutdown times of the belt conveyor are determined, forming an energy-saving scheduling scheme that can be used to guide the organization of terminal operations.

[0068] S3-1: Instantiation and Solution Preparation of the Scheduling Model. Based on the data collected in S1, the energy-saving scheduling optimization model constructed in S2 is instantiated and prepared for solution. Specifically, the operation time axis is divided according to the start and end times of the planning period and the arrival time of the tasks, and the time set and time step are determined; the information of material piles, stack positions, and stacks in the stockyard is mapped to the set and parameters related to stockyard location allocation; the optional transportation paths between the tippler, ship loader, and material piles, and the belt conveyor sequence on them, are mapped to the set and parameters related to task transportation path selection and belt conveyor allocation; the operation type, earliest start time, operation volume, and corresponding stacks of each task, as well as the unit time energy consumption, start-up time, shutdown time, unit cargo transportation time, and maximum number of simultaneous starts of each belt conveyor are written into the model parameters. Based on this, taking the weighted sum of the total task operation time and the total energy consumption of the belt conveyor system as the optimization objective, and following the objective function form and various constraint structures given in S2, a mixed integer programming framework consisting of a scheduling objective function and constraints is constructed. Then, the above set and parameters are substituted into the objective function and various constraint structures given in S2 to obtain the energy-saving scheduling optimization problem for the current planning period. The population size, maximum number of iterations, and convergence criteria required for the hybrid intelligent solution are set to provide complete model inputs and algorithm parameters for subsequent solutions.

[0069] S3-2: Hybrid Intelligent Solution Based on Improved Genetic Algorithm and Mixed Integer Programming. Building upon the energy-saving scheduling optimization problem instantiated in S3-1, a hybrid intelligent solution strategy combining an improved genetic algorithm and mixed integer programming is adopted to jointly optimize yard location allocation, task transportation path selection, and conveyor start-up and shutdown timing. Specifically, using stacking location allocation, task transportation path selection, and conveyor start-stop control schemes as the encoding structure, the discrete decision variables in S2 are represented by chromosomes to construct an initial population. The weighted sum of the total task operation time and the total energy consumption of the conveyor system in the S2 objective function is used as the fitness function to evaluate individuals in the population. Based on this, selection, crossover, and mutation operations are repeatedly performed to evolve the yard layout, path combinations, and start-stop strategies within the feasible solution space. In each generation, the discrete decision structure of individuals with better fitness is fixed, retaining only continuous time variables such as task start time, conveyor start time, and shutdown time, as well as energy consumption variables. The mixed-integer programming solution module is called to perform local fine-tuning of the energy-saving scheduling optimization problem in S3-1, obtaining an improved solution under the current structure. The local optimization results are written back to the population and the fitness is updated. When the optimal fitness improvement is insufficient for several consecutive generations or the preset number of iterations is reached, the evolution process is terminated, and the individual with the lowest fitness is selected as the optimal or near-optimal scheduling solution that satisfies all constraints of S2.

[0070] S3-3: Based on the optimal or near-optimal solution obtained in S3-2, generate a specific operation scheduling plan for the planning period. According to the values ​​of variables related to the allocation of yard location, determine the pile number and starting pile position number corresponding to each stockpiling task, giving the spatial location of the pile in the yard; according to the values ​​of variables related to task transportation path selection, determine the specific path of each stockpiling and retrieving task in the set of optional transportation paths, and determine the sequence of conveyor belts traversed by each task based on the conveyor belt sequence on the path; according to the values ​​of time-related variables and variables related to conveyor belt start / stop and dynamic switching, determine the start time, completion time, start transportation time on each conveyor belt for each task, as well as the start time, stop time, and switching mode (continuous operation or shutdown / restart) of each conveyor belt during the planning period. By integrating the above yard location allocation results, transportation path selection results, and conveyor belt start / stop timing results, an energy-saving operation scheduling plan for the dry bulk terminal covering the entire planning period is formed to guide the terminal operator in formulating stockpiling plans, retrieving plans, and conveyor belt start / stop control plans.

[0071] This invention also provides an automated dry bulk cargo terminal energy-saving scheduling system that considers dynamic switching of belt conveyors, used to implement the above-mentioned automated dry bulk cargo terminal energy-saving scheduling method. The automated dry bulk cargo terminal energy-saving scheduling system is deployed on a computer device with a processor and a memory. The memory stores a computer program. When the computer program is called and executed by the processor, the above-mentioned method steps S1 to S3 are processed in modules to form corresponding functional units. The automated dry bulk cargo terminal energy-saving scheduling system that considers dynamic switching of belt conveyors includes a data acquisition module, a model building module, an optimization solution module, and a scheduling scheme generation module, specifically:

[0072] The data acquisition module is used to acquire yard characteristic data, stacking and retrieving task information, and operating equipment parameter information from the automated dry bulk terminal site. According to the requirements of method step S1, the data acquisition module collects and preprocesses the quantity, location, length, stacking position division information, and stacking identifiers of the stockpiles in the yard; collects the optional transport paths between the tippler, ship loader, and each stockpile and their corresponding conveyor belt sequences; collects the operation type, earliest start time, workload, and corresponding stacking information for each stacking and retrieving task; and collects parameters such as unit time energy consumption, start-up time, shutdown time, unit cargo transport time, and maximum simultaneous start quantity for each conveyor belt. The module cleans, formats, and classifies the above data for storage, providing a consistent data foundation for the construction and optimization of the scheduling model.

[0073] The model building module is used to construct a mixed integer programming model for energy-saving scheduling of dry bulk cargo terminals, considering the dynamic switching of conveyor belts, based on the input data provided by the data acquisition module. Following the modeling concept of step S2, the module divides the operation time axis according to the start and end times of the planning period and the arrival times of tasks, determining the time set and time step. It maps the information of stockpiles, stack locations, and stacks in the yard to sets and parameters related to yard location allocation. It maps the optional transport paths between tippers, ship loaders, and stockpiles, and their corresponding conveyor belt sequences, to sets and parameters related to task transport path selection and conveyor belt allocation. The module also writes the operation type, earliest start time, workload, and corresponding stacks for each task, as well as the energy consumption and start / stop parameters of each conveyor belt into the model parameters. Based on this, taking the weighted sum of the total task operation time and the total energy consumption of the belt conveyor system as the optimization objective, and following the objective function form given in step S2-1 and the constraints given in S2-2 to S2-7, such as the yard space allocation constraint, task transportation path selection constraint, belt conveyor operation sequence and time continuity constraint, belt conveyor start-up and dynamic switching logic constraint, start-up quantity and power limit constraint, and variable domain constraint, a mixed integer programming framework consisting of the scheduling objective function and constraints is constructed to form an example of the energy-saving scheduling optimization problem for the current planning period.

[0074] The optimization solution module is used to jointly optimize the yard location allocation, task transportation path selection, and conveyor start-up and shutdown timing based on the mixed integer programming framework established by the model building module, using a hybrid intelligent solution strategy. The optimization solution module corresponds to the implementation of step S3-2, specifically by: using the stacking location allocation, task transportation path selection, and conveyor start-up and shutdown control schemes as the encoding structure, representing the discrete decision variables in S2 with chromosomes to construct an initial population; using the weighted sum of the total task operation time and the total energy consumption of the conveyor system in the scheduling objective function as the fitness function to evaluate the individuals in the population, and performing a global search in the feasible solution space through selection, crossover, and mutation operations; fixing the yard location allocation, path selection, and start-up and shutdown decision structures of individuals with better fitness, retaining only continuous time variables such as task start time, conveyor start time, and shutdown time, and energy consumption variables, and calling the mixed integer programming solver to perform local fine-tuning of the energy-saving scheduling optimization problem; when the preset convergence conditions are met, outputting the optimal or near-optimal scheduling solution that satisfies all constraints in S2.

[0075] The scheduling scheme generation module is used to generate specific energy-saving operation scheduling schemes for the planning period based on the optimal or near-optimal solution results output by the optimization solution module. The scheduling scheme generation module corresponds to implementation steps S3-3. Based on the values ​​of relevant variables assigned to the yard location, it determines the stockpile number and starting position number of each stockpile task, giving the spatial location of the stockpile in the yard. Based on the values ​​of relevant variables selected for task transportation paths, it determines the specific transportation path for each stockpile and retrieving task within the set of selectable transportation paths, and determines the sequence of conveyor belts traversed by each task based on the conveyor belt sequence along the path. Based on the values ​​of time-related variables and variables related to conveyor belt start / stop and dynamic switching, it determines the start time, completion time, and start transportation time on each conveyor belt for each task, as well as the start time, stop time, and switching mode (continuous operation or shutdown / restart) of each conveyor belt during the planning period. This forms an energy-saving operation scheduling scheme for the dry bulk cargo terminal covering the entire planning period, and outputs it in report or data interface form for the terminal operator to use in formulating stockpile plans, retrieving plans, and conveyor belt start / stop control plans.

[0076] The beneficial effects of this invention are:

[0077] This invention constructs a hybrid integer programming model for the coordinated optimization of task scheduling and conveyor start-stop operations, and combines an improved genetic algorithm with a hybrid intelligent solution strategy of hybrid integer programming. This achieves integrated optimization of yard location allocation, task transport path selection, and conveyor start-stop timing in multi-task, multi-transportation-path scenarios. Through precise modeling and control of conveyor start-stop behavior, it effectively reduces idle operation and repeated start-stop phenomena, significantly reducing operational energy consumption while ensuring operational efficiency. This provides technical support for intelligent collaboration and green, efficient operation of dry bulk terminals. Attached Figure Description

[0078] Figure 1 This is a schematic diagram of the traditional process of starting and stopping the conveyor belts in the conventional design of an automated dry bulk terminal, where the flow direction is "downstream first, then upstream". A represents the direction of cargo flow, and B represents the direction of the starting sequence of the conveyor belts on the transportation path.

[0079] Figure 2 This is a diagram showing the layout and operating equipment of a dry bulk cargo terminal yard.

[0080] Figure 3 A schematic diagram of an energy-saving dispatching system for an automated dry bulk cargo terminal that takes into account the dynamic switching of belt conveyors.

[0081] Figure 4 This is a flowchart of the method of the present invention.

[0082] In the diagram: 1 tippler, 2 belt conveyor, 3 transfer tower, 4 stockpile, 5 reclaimer, 6 stacker, 7 ship loader. Detailed Implementation

[0083] The present invention will be further described below with reference to specific implementation examples.

[0084] An energy-saving scheduling method for automated dry bulk cargo terminals that considers dynamic switching of belt conveyors, such as... Figure 4 As shown, it includes the following steps:

[0085] S1: Data Acquisition and Model Input Preparation. In this embodiment, firstly, following step S1 of the invention, characteristic data of the automated dry bulk terminal yard, stacking operation task information, reclaiming operation task information, and operation equipment parameter information are collected as inputs for subsequent model construction and solving.

[0086] like Figure 2 As shown, the scheduling planning period is 48 hours. There are four parallel stockpiles in the yard, and each stockpile is divided into five storage locations along its length. Combined with... Figure 2 In this embodiment, the relevant data for the stockpile includes: the number, spatial location, and layout of each conveyor belt and transfer tower; the number, spatial location, length, and storage capacity of the four stockpiles; the number and number of the five stacking positions divided along the length of each stockpile; the set of selectable cargo transportation routes between the tipper, each stockpile, and the ship loader; and the sequence and number of conveyor belts included in each transportation route.

[0087] In this embodiment, the task transportation path and its conveyor belt structure are shown in Table 1. Table 1 provides the path attributes (stockpiling path or retrieving path), the conveyor belt number on the path, the transportation time required for a unit quantity of goods to pass through the path, and the reachable stockpile number for each path, which are used to construct the transportation path set and path reachability parameters.

[0088] Table 1: Task Transportation Route and Belt Conveyor Structure

[0089]

[0090] The stacking task information and retrieving task information are used to characterize the transportation process of goods between different work points. The stacking task represents the process of goods being transported from the tipper to the yard for stacking, and the retrieving task represents the process of goods being transported from the yard stack to the ship loader. In this embodiment, there are a total of 4 tasks to be processed during the planning period, including 2 stacking tasks and 2 retrieving tasks. The task information is shown in Table 2.

[0091] Table 2: Task Information

[0092]

[0093] In addition, parameters such as the number, start and end connection positions, length, rated conveying capacity, rated operating speed, rated power, start-up time and energy consumption, shutdown time, and time required for a unit quantity of goods to be transported along the conveyor are collected for each belt conveyor. Parameters such as the equipment number and rated operating capacity of stacker cranes, reclaimers, tippers, and ship loaders are also collected. These data collectively constitute the yard characteristic data, task information, and operating equipment parameter information mentioned in step S1, forming the basis for the subsequent construction of the energy-saving scheduling optimization model.

[0094] S2: Construction of Energy-Saving Scheduling Optimization Model. Based on the data collected in S1 above, this embodiment constructs an energy-saving scheduling optimization model for an automated dry bulk terminal, considering the dynamic switching of conveyor belts, according to the modeling concept in step S2 of the invention. This model comprehensively describes the coupling relationship between yard location allocation, task transportation path selection, and conveyor belt start-stop control, with the objective of minimizing the weighted sum of total task operation time and total conveyor belt energy consumption.

[0095] S2-1: Objective Function and Calculation of Task Time and Energy Consumption. First, an objective function is constructed for an energy-saving scheduling optimization model that considers the dynamic switching of the belt conveyor, used to comprehensively measure time and energy costs. Specifically, the optimization objective is to minimize the weighted sum of the operation time of all tasks from the earliest possible start time to the actual completion time, and the energy consumption required for the belt conveyor to complete all tasks.

[0096] In this embodiment, the objective function still adopts formula (1) given in the invention content section, and the calculation relationship of task completion time and the calculation relationship of task energy consumption on the belt conveyor adopt formula (2) and formula (3) respectively. By substituting the task arrival time, stacking or retrieving operation time in Table 2, and the path transportation time and belt conveyor energy consumption parameters in Table 1 into formulas (1) to (3), the calculation expressions of task time cost and belt conveyor energy consumption cost under this embodiment can be obtained.

[0097] S2-2: Spatial location allocation constraints in the stockpile. Based on the objective function and the calculation relationship between task completion time and conveyor energy consumption given in S2-1, this embodiment further establishes relevant constraints on the spatial location allocation in the stockpile to characterize the spatial distribution of stacks on each stockpile and its stack position.

[0098] In this embodiment, the stockpile has 4 stockpiles, each with 5 stacking positions. All stockpiling tasks need to be allocated within this limited space. The spatial location allocation constraints of the stockpile still adopt the formulas (4) to (8) given in the content of the invention, which respectively ensure that: each stack must be and can only be allocated to a certain set of consecutive stacking positions on a stockpile; the space occupied by the stack does not exceed the length boundary of the stockpile; the spatial and temporal relationship between any two stacks on the same stockpile satisfies the logical conditions of non-overlapping, non-contamination, and first-pile-then-retrievable.

[0099] In this embodiment, the “number of stacks occupied in the yard” in Table 2 is mapped to the stack length, and the division information of the No. 4 material pile and its 5 stack positions is mapped to the material pile set and the stack position set. The stacking tasks 1 and 2 are constrained to form two stacks on the No. 4 material pile through formulas (4) to (8), and the corresponding material retrieval tasks 3 and 4 are ensured to be later than the sum of the stacking completion time and the transportation time, so as to achieve the coordination of stacking and retrieval in space and time.

[0100] S2-3: Task Transportation Route Selection Constraints. Based on the above objective function and yard spatial allocation constraints, this embodiment establishes relevant constraints for task transportation route selection.

[0101] The task transportation route selection constraints still use formulas (9) to (11): each task must and can only select one transportation route; stacking tasks can only select stacking routes, and picking tasks can only select picking routes; when a certain route cannot reach the stack of materials corresponding to the task, the task is prohibited from selecting that route. The specific accessibility parameters are determined by the "accessible stack number" in Table 1.

[0102] In this embodiment, when the material stacking task selects path 3 or path 4, the corresponding reachable material pile number is 3 or 4. When the optimization result determines that there is material pile number 4 in material stacking tasks 1 and 2, both path 3 and path 4 can be used as feasible material stacking paths, while other material stacking paths that cannot reach material pile number 4 are constrained to be unselectable.

[0103] S2-4: Belt Conveyor Operation Sequence and Time Continuity Constraints. Based on the task transportation path selection constraints given in S2-3, this embodiment establishes belt conveyor operation sequence and time continuity related constraints to characterize the allocation relationship and time evolution process of each task in the belt conveyor system. The belt conveyor operation sequence and time continuity constraints still adopt formulas (12) to (21), including: associating the selection of the task on the transportation path with its allocation on each belt conveyor; introducing virtual start tasks and virtual end tasks on each belt conveyor to ensure the integrity and uniqueness of the task execution sequence; and combining the task start time with the start transportation time of the first belt conveyor on the path, the belt conveyor start time, and the transportation time between adjacent belt conveyors to ensure the continuous flow of task cargo in the time dimension, and not earlier than the earliest start time and the belt conveyor start completion time.

[0104] In this embodiment, the belt conveyor sequence on each path given in Table 1 (e.g., belt conveyors 2 and 7 on path 4, and belt conveyors 8 and 12 on path 9) is mapped to the path-belt conveyor correspondence in formulas (12) to (21). These constraints ensure that the start time of the task on each belt conveyor in the path is consistent.

[0105] S2-5: Logic Constraints for Belt Conveyor Start-up and Dynamic Switching. Based on the belt conveyor operation sequence and time continuity constraints given in S2-4, this embodiment establishes logic constraints related to belt conveyor start-up and dynamic switching to characterize the start-up and stop operations of the belt conveyor between adjacent tasks. The logic constraints for belt conveyor start-up and dynamic switching adopt formulas (22) to (27), which uniformly describe the time evolution process of the belt conveyor under two working conditions: continuous operation and shutdown restart, by giving the downtime of the belt conveyor after completing a certain task, the start-up and shutdown connection relationship between adjacent tasks, and the start-up and shutdown state conditions of the first and last tasks.

[0106] In this embodiment, the start-up time, shutdown time, and energy consumption per unit time of the belt conveyor are given by the equipment parameters. Through formulas (22) to (27), the model can select to maintain continuous operation or perform shutdown and restart based on the interval time of adjacent tasks on the same belt conveyor, and feed back the energy consumption difference under different start-up and shutdown strategies to the objective function, thereby realizing belt conveyor energy-saving control that takes into account dynamic switching behavior.

[0107] S2-6: Belt Conveyor Start-up Quantity and Power Limit Constraints. Based on S2-4 and S2-5, this embodiment further establishes constraints related to the belt conveyor start-up quantity and power limit to avoid excessive power load exceeding the upper limit due to too many belt conveyors starting at the same time. The belt conveyor start-up quantity and power limit constraints adopt formulas (28) to (31). By discretely marking the belt conveyor start-up time period, the start-up time interval of the belt conveyor under each task is linked with the start-up state variable: the number of belt conveyors in the start-up state at any discrete time does not exceed the capacity upper limit; the start-up process of a task on a certain belt conveyor covers the start-up duration in time; when a certain time is marked as "starting", that time must fall between the start-up time and start-up end time of the corresponding task's belt conveyor.

[0108] In this embodiment, considering the capacity of the dock substation and the operation of other electrical equipment, the maximum number of belt conveyors allowed to start at the same time is given as 6. The belt conveyor starting behavior is restricted by formulas (28) to (31), so that the solution meets the energy-saving target and the power system operation safety requirements.

[0109] S2-7: Variable Domain Constraints and Model Completeness. Finally, based on the objective functions and various constraints given in S2-1 to S2-6, this embodiment further establishes variable domain constraints to ensure the completeness of the mathematical model definition. The variable domain constraints adopt formulas (32) to (36), which respectively limit the non-negativity of the belt conveyor energy consumption variable, the values ​​of various time-related variables to the given discrete time set, and the 0-1 discrete characteristics of variables such as path selection, belt conveyor allocation, yard location allocation, stacking relationship, task sequence, start-stop logic and start-up status.

[0110] This results in a mixed-integer programming model that integrates yard location allocation, task transportation route selection, and conveyor start / stop control, providing a mathematical foundation for subsequent solutions.

[0111] S3: Model Solving and Scheduling Scheme Generation. After completing the energy-saving scheduling optimization model constructed in S2, this embodiment solves the model according to step S3 and generates a job scheduling scheme for the planning period based on the solution results.

[0112] S3-1: Instantiation and Solution Preparation of the Scheduling Model. Based on the data collected in S1, the energy-saving scheduling optimization model constructed in S2 is instantiated and prepared for solution. Specifically, according to the start and end times of the 48-hour planning period in this embodiment and the earliest start time of the tasks in Table 2, the operation time axis is divided to determine the time set and time step; Figure 2 The four material piles and the five stacking positions of each material pile shown are mapped to the set and parameters related to the allocation of the stockyard location. The optional transportation paths between the tipper, ship loader and material pile in Table 1 and the belt conveyor sequence on them are mapped to the set and parameters related to the selection of task transportation paths and belt conveyor allocation. The operation type, earliest start time, operation volume and corresponding stacking data of each task in Table 2, as well as the unit time energy consumption, start-up time, shutdown time, unit cargo transportation time and maximum number of simultaneous starts of each belt conveyor are written into the model parameters.

[0113] Based on this, taking the weighted sum of the total task operation time and the total energy consumption of the belt conveyor system as the optimization objective, and following the objective function form and various constraint structures given in S2, a mixed integer programming framework consisting of the scheduling objective function and constraints is constructed to obtain the energy-saving scheduling optimization problem for the current planning period. Simultaneously, the population size, maximum number of iterations, and convergence criterion required for the hybrid intelligent solution are set, providing complete model inputs and algorithm parameters for subsequent solutions.

[0114] S3-2: Hybrid Intelligent Solution Based on Improved Genetic Algorithm and Mixed Integer Programming. Building upon the energy-saving scheduling optimization problem instantiated in S3-1, this embodiment employs a hybrid intelligent solution strategy combining an improved genetic algorithm (IGA) and mixed integer programming (MIP) to jointly optimize yard location allocation, task transportation path selection, and conveyor start-stop timing. Specifically, using the stacking location allocation, task transportation path selection, and conveyor start-stop control scheme as the encoding structure, the discrete decision variables in S2 are represented by chromosomes to construct an initial population. The weighted sum of the total task operation time and the total energy consumption of the conveyor system in the objective function of S2 is used as the fitness function to evaluate the individuals in the population. Based on this, selection, crossover, and mutation operations are repeatedly performed to evolve the yard layout, path combinations, and start-stop strategies within the feasible solution space.

[0115] In each generation, the discrete decision structure of the individuals with better fitness is fixed, retaining only continuous time variables such as task start time, conveyor start time, and downtime, as well as energy consumption variables. The mixed-integer programming solution module is then used to perform local fine-tuning of the energy-saving scheduling optimization problem in S3-1, obtaining a better time and energy allocation result under the current structure. As iterations proceed, the scheduling scheme corresponding to the individual with the best fitness gradually converges. When the improvement in the best fitness is insufficient for several consecutive generations or the preset maximum number of iterations is reached, the evolution process is terminated, and the individual with the lowest fitness is selected as the optimal or near-optimal scheduling solution that satisfies all constraints in S2.

[0116] S3-3: Scheduling Result Output and Scheme Analysis. Based on the optimal or near-optimal solution obtained in S3-2, this embodiment generates a specific operation scheduling scheme for the planning period. According to the values ​​of variables related to the stockpile location allocation, the stockpile number and starting position number of each stockpile task are determined, giving the spatial location of the stockpile in the stockpile. According to the values ​​of variables related to task transportation path selection, the specific paths of each stockpile and retrieving task in the set of optional transportation paths are determined, and the sequence of conveyor belts traversed by each task is determined in conjunction with the conveyor belt sequence on the path. According to the values ​​of time-related variables and variables related to conveyor belt start / stop and dynamic switching, the start time, completion time, and start transportation time on each conveyor belt for each task are determined, as well as the start time, stop time, and switching mode (continuous operation or shutdown / restart) of each conveyor belt during the planning period.

[0117] In this embodiment, the task operation scheme obtained by the above method is shown in Table 3:

[0118] Table 3: Task Operation Plan

[0119]

[0120] The operation plan for each conveyor belt is as follows: Conveyor belt 1 starts at time step 84 and stops completely at time step 109; Conveyor belt 2 starts at time step 5 and stops completely at time step 38; Conveyor belt 7 starts at time step 20, stops completely at time step 58, restarts at time step 99, and stops completely at time step 137, meaning a shutdown and restart operation occurs between transport tasks 1 and 2; Conveyor belt 8 starts at time step 98, runs continuously after completing task 3 at time step 150, stops completely at time step 193 after transporting task 4; Conveyor belt 12 starts at time step 127, shuts down at time step 158, restarts at time step 177, and shuts down at time step 199, meaning a shutdown and restart operation occurs between transport tasks 3 and 4.

[0121] This embodiment also provides an automated dry bulk cargo terminal energy-saving scheduling system that considers dynamic switching of belt conveyors, such as... Figure 3 As shown, it includes a data acquisition module, a model building module, an optimization solution module, and a scheduling scheme generation module. The functions of each module are as follows:

[0122] The data acquisition module is used to acquire yard characteristic data, stacking and retrieving task information, and operating equipment parameter information from the automated dry bulk terminal site, corresponding to step S1 of the method. This module collects and preprocesses information on the quantity, location, length, and stack division of stockpiles and stack identifiers in the yard through database interfaces, equipment sensors, or manual input. It also collects the optional transport paths between tippers, ship loaders, and each stockpile, as well as the sequence of conveyor belts on them. Furthermore, it collects the operation type, earliest start time, workload, and corresponding stacking information for each stacking and retrieving task, and parameters such as energy consumption per unit time, start-up time, shutdown time, transport time per unit cargo volume, and maximum number of simultaneous starts for each conveyor belt. The module cleans, formats, and classifies the above data for storage, providing a unified data foundation for scheduling model construction and optimization solutions.

[0123] Model building module: This module is used to build a mixed integer programming model for energy-saving scheduling of dry bulk cargo terminals, considering the dynamic switching of belt conveyors, based on the input data provided by the data acquisition module, and implements step S2. This module follows the modeling ideas of S2-1 to S2-7, divides the operation time axis according to the start and end times of the planning period and the arrival time of the task, and determines the time set and time step; maps the information of material piles, stacks and stacks in the yard to the relevant set and parameters of yard location allocation, maps the optional transportation paths between tippers, ship loaders and material piles and their belt conveyor sequences to the relevant set and parameters of task transportation path selection and belt conveyor allocation, and writes the operation type, earliest start time, operation volume and corresponding stacks of each task, as well as the energy consumption and start / stop parameters of each belt conveyor into the model parameters. On this basis, the weighted sum of the total operation time of the task and the total energy consumption of the belt conveyor system is used as the optimization objective. According to formulas (1) to (36), a mixed integer programming framework consisting of scheduling objective function and constraints is constructed to form an example of energy-saving scheduling optimization problem for the current planning period.

[0124] The optimization solution module, based on the mixed-integer programming framework established in the model building module, employs a hybrid intelligent solution strategy to jointly optimize the yard location allocation, task transportation path selection, and conveyor start-up and shutdown timing, corresponding to step S3-2. This module uses the stacking location allocation, task transportation path selection, and conveyor start-up and shutdown control schemes as the encoding structure, representing discrete decision variables using chromosomes to construct an initial population. It evaluates individuals in the population using the weighted sum of the total task operation time and the total energy consumption of the conveyor system in the scheduling objective function, and performs a global search within the feasible solution space through selection, crossover, and mutation operations. For individuals with better fitness, their yard location allocation, path selection, and start-up / shutdown decision structures are fixed, retaining only continuous time variables such as task start time, conveyor start time, and shutdown time, as well as energy consumption variables. The mixed-integer programming solver is then invoked to perform local fine-tuning of the energy-saving scheduling optimization problem. When the preset convergence conditions are met, the optimal or near-optimal scheduling solution that satisfies all constraints in S2 is output.

[0125] The scheduling scheme generation module generates specific energy-saving operation scheduling schemes for the planning period based on the optimal or near-optimal solution results output by the optimization solution module, corresponding to implementation steps S3-3. This module assigns values ​​to relevant variables based on the location of the stockyard, determines the stockpile number and starting position number of each stockpile task, and provides the spatial location of the stockpile in the stockyard; it determines the specific transportation path of each stockpile and retrieving task within the set of optional transportation paths based on the values ​​of relevant variables for task transportation path selection, and determines the sequence of conveyor belts traversed by each task based on the conveyor belt sequence along the path; based on the values ​​of time-related variables and variables related to conveyor belt start / stop and dynamic switching, it determines the start time, completion time, and start transportation time on each conveyor belt for each task, as well as the start time, stop time, and switching mode (continuous operation or shutdown / restart) of each conveyor belt during the planning period, thereby forming an energy-saving operation scheduling scheme for the dry bulk terminal covering the entire planning period. This scheme is output in report or data interface form for the terminal operator to use in formulating stockpile plans, retrieving plans, and conveyor belt start / stop control plans.

[0126] It should be noted that the above embodiments are merely illustrative of the implementation methods of the present invention, but should not be construed as limiting the scope of the present invention. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. An energy-saving scheduling method for automated dry bulk cargo terminals considering dynamic switching of belt conveyors, characterized in that: The automated dry bulk terminal energy-saving scheduling method includes the following steps: S1: Collect characteristic data of automated dry bulk terminal yards, stacking operation task information, reclaiming operation task information, and operation equipment parameter information; S2: Based on the yard characteristic data, stockpiling task information, reclaiming task information and operating equipment parameter information obtained in S1, construct an energy-saving scheduling optimization model for automated dry bulk cargo terminals that considers dynamic switching of belt conveyors. First, an objective function is established with the goal of minimizing the weighted sum of task completion time and overall energy consumption of the conveyor belt. Then, constraints are constructed from the aspects of yard space allocation, task transportation route selection, conveyor belt operation sequence and time continuity, and energy consumption of conveyor belt start-up, shutdown and dynamic switching, forming an energy-saving scheduling optimization model for automated dry bulk terminal that considers the dynamic switching of conveyor belts. S3: Solve the energy-saving scheduling optimization model of the automated dry bulk terminal constructed in S2, and generate the operation scheduling scheme within the planning period based on the solution results; First, the energy-saving scheduling optimization model for automated dry bulk cargo terminals is instantiated and prepared for solution. The input data in S1 is mapped to the sets, parameters, and initial decision space in the model, and a mixed integer programming framework consisting of the scheduling objective function and constraints is constructed. Then, a hybrid intelligent solution strategy combining the improved genetic algorithm IGA and mixed integer programming MIP is adopted to jointly optimize the allocation of yard locations, selection of task transportation routes, and start-up and shutdown timing of belt conveyors, obtaining the optimal solution that satisfies the constraints. Finally, based on the solution results, the storage location of each stockpiling task in the yard, the start time of each task, the selection of transportation routes, and the start-up and shutdown times of belt conveyors are determined, forming an energy-saving scheduling scheme to guide the organization of terminal operations.

2. As described in claim 1 An energy-saving scheduling method for automated dry bulk cargo terminals considering dynamic switching of belt conveyors, characterized in that: In S1, the characteristic data of the automated dry bulk terminal yard includes: the number, spatial location and layout of each conveyor belt and transfer tower; the number, number, spatial location, length and storage capacity of each stockpile; the number and number of stacking positions divided along the length of each stockpile; the set of cargo transportation paths between each stockpile and the tippler; the set of cargo transportation paths between each stockpile and the ship loader; and the sequence and number of conveyor belts included in each transportation path.

3. As described in claim 1 An energy-saving scheduling method for automated dry bulk cargo terminals considering dynamic switching of belt conveyors, characterized in that: In S1, the stacking task information and retrieving task information data are used to characterize the transportation process of goods between different work points; including: task quantity, number and task type; the amount of goods transported for each task, the number of stacking positions occupied, the earliest start time of operation, and the priority of different tasks.

4. As described in claim 1 An energy-saving scheduling method for automated dry bulk cargo terminals considering dynamic switching of belt conveyors, characterized in that: In S1, the equipment parameter information data includes: the number, start and end connection position, length, rated conveying capacity, rated operating speed, rated power, start-up time and energy consumption, shutdown time, and the time required for a unit quantity of goods to be transported along the conveyor belt; the equipment number and rated operating capacity of the stacker, reclaimer, tipper, and ship loader.

5. The method according to claim 1 An energy-saving scheduling method for automated dry bulk cargo terminals considering dynamic switching of belt conveyors, characterized in that: Specifically, S2 is: S2-1: First, construct the objective function of the energy-saving scheduling optimization model of the automated dry bulk terminal considering the dynamic switching of the belt conveyor, which is used to comprehensively measure the time cost and energy cost of the automated dry bulk terminal under a given yard layout and operation plan; specifically, the optimization objective is to minimize the weighted sum of the operation time of all tasks from the earliest start time to the actual completion time, and the energy consumption required by the belt conveyor to complete all tasks; respectively give the calculation relationship of task completion time and the calculation relationship of energy consumption of task on the belt conveyor, and establish the objective function accordingly, as shown in formulas (1) to (3); (1) ； (2) ； (3) ； In the formula, Represents a set of tasks. Indicates the task number; Weighting coefficients representing task completion time; Indicates task Completion time; Indicates task The earliest start time of the work; The weighting coefficient representing the energy consumption of the belt conveyor; Indicates a collection of belt conveyors. Indicates the belt conveyor number; Indicates completion of task belt conveyor Energy consumption; Indicates task The start date of the project; Indicates task The time required to operate on the stacker or reclaimer; Indicates the route taken by a unit quantity of goods. Required shipping time; Represents an infinitely large constant; Represents the set of transportation routes. Indicates the transport route number; It is a 0-1 variable, representing the state when the task... Select transportation route The value is 1 if it is 1, otherwise it is 0. Indicates belt conveyor The rated power, i.e., the energy consumption required to operate per unit time; These respectively represent the completion of the task. Rear belt conveyor End of runtime; Indicates belt conveyor Shutdown time; For 0-1 decision variables, when the task is completed Rear belt conveyor The value is 1 if an immediate shutdown operation is performed, and 0 otherwise. Indicates completion of task belt conveyor Startup time; Indicates belt conveyor Booting time; A variable of 0-1, representing the state during task execution. Front belt conveyor If the power-on process has been completed, the value is 1; otherwise, it is 0. It is also a 0-1 variable, representing the state when the task... On the belt conveyor If transportation is carried out, the value is 1; otherwise, it is 0. The objective function is shown in Equation (1), the task completion time is calculated as shown in Equation (2), and the belt conveyor energy consumption is calculated as shown in Equation (3); S2-2: Establish relevant constraints for the spatial location allocation of the stockpile to characterize the spatial distribution of stacks on each stockpile and its location; The constraints related to the allocation of space in the stockpile include: each stack must and can only be assigned to a set of consecutive stack positions on a material pile, as shown in formula (4); the space occupied by the stack in the material pile cannot exceed the length boundary of the material pile, as shown in formula (5); formula (6) imposes logical constraints on the spatial and temporal relationships between any two stacks on the same material pile to avoid simultaneous spatial and temporal overlap at the same location; under the premise of a given spatial order, it forces the reservation of at least one stack position space between different stacks, as shown in formula (7); the stacking task and the picking task corresponding to the same stack satisfy the temporal order relationship, and the start time of the picking task is not earlier than the sum of the transportation time required to complete the corresponding stacking task, as shown in formula (8). (4) ； (5) ； (6) ； (7) ； (8) ； In the formula, Indicates a collection of material piles. Indicates the stockpile number; Indicates a stockpile The stacking station set on the top, Indicates the stack location number; It is a 0-1 variable, indicating if stacking Assigned to stockpile And the leftmost stack position it occupies is The value is 1 if it is 1, otherwise it is 0. Represents a stacked set. and Indicates the number of different stockpiles; Indicates stacking The length occupied on the material pile Indicates a stockpile Length; and These are 0-1 variables, representing the values ​​in the stockpile. Stacking and Spatial relationships and temporal sequence: when in the stockpile superior The occupied stack space is When on the left It is 1 if it is not 0 otherwise; when in the stockpile superior Prior to When occupying stack space It is 1 if it is 1, otherwise it is 0; similarly, and It is also a 0-1 variable: when in the stockpile superior The occupied stack space is When on the left It is 1 if it is not 0 otherwise; when in the stockpile superior Prior to When occupying stack space It is 1 if it is true, otherwise it is 0; It is a 0-1 variable, indicating if stacking Assigned to stockpile And the leftmost stack position it occupies is The value is 1 if it is 1, otherwise it is 0. and Representing tasks and The start time of the work; Indicates task Operating time on stacker or reclaimer; Represents the set of transportation routes. Indicates the transport route number; Indicates the route taken by a unit quantity of goods. Required shipping time; and These represent the sets of material stacking tasks and material retrieving tasks, respectively; they are tasks. a subset of and Indicates the task number; It is a 0-1 variable, when the task Select transportation route The value is 1 if the condition is met, and 0 otherwise. A 0-1 variable used to represent the state of the material pile. Above and Retrieving Tasks Corresponding stacks and stockpiling tasks Corresponding stacks The order of time between them, when related to the material stacking task Corresponding stacks Prior to the material collection task in terms of time. Corresponding stacks Complete the task, that is, when the same stack of materials is stacked first and then retrieved. It is 1 if it is true, otherwise it is 0; S2-3: Establish relevant constraints for mission transportation route selection; The constraints related to the selection of the task transportation path include: each task must and can only select one transportation path, as shown in formula (9); stacking tasks can only select stacking paths and picking tasks can only select picking paths to ensure the consistency of task type and path attributes, as shown in formula (10); when a certain transportation path cannot reach the material pile where the corresponding stack of the task is located, the task is prohibited from selecting that transportation path, as shown in formula (11). (9) ； (10) ； (11) ； In the formula, Represents a set of tasks. and These represent the sets of material stacking tasks and material retrieving tasks, respectively. a subset of Indicates the task number; Represents the set of transportation routes. and The sets representing the stacking paths and the retrieving paths are sets. a subset of Indicates the transport route number; It is a 0-1 variable, representing the state when the task... Select transportation route The value is 1 if the condition is met, and 0 otherwise. It is an infinitely large constant; Indicates a collection of material piles. Indicates the stockpile number; Indicates a stockpile The stacking station set on the top, Indicates the stack location number; It is a 0-1 variable, representing the task. Corresponding stacks If assigned to the stockpile And the leftmost stack position it occupies is The value is 1 if it is 1, otherwise it is 0. For path reachability parameters, if the transportation path Able to reach the stockpile The value is 1 if the value is 1, otherwise the value is 0. S2-4: Establish constraints related to the operation sequence and time continuity of the belt conveyor to characterize the allocation relationship and time evolution process of each task in the belt conveyor system; The constraints related to the operation sequence and time continuity of the belt conveyor include: using formula (12) to associate the selection of the task on the transportation path with its allocation on each belt conveyor, ensuring that when a task selects a transportation path that includes a certain belt conveyor, the corresponding belt conveyor on that path is selected; using formulas (13) to (16) to introduce a starting virtual task and a ending virtual task on each belt conveyor, constraining the connection between the real tasks on the belt conveyor, ensuring the operation sequence of tasks on the same belt conveyor; the task start time is the time when the task starts transportation on the selected path, as shown in formula (17); the task start time cannot be earlier than the earliest start time, nor earlier than the start completion time of the first belt conveyor on the selected path of the task, as expressed by formulas (18) and (19) respectively; the task must be completed before it can start transportation on the belt conveyor, as expressed by formula (20); the continuity of cargo flow between belt conveyors on the same transportation path must be ensured, as expressed by formula (21), the specific formulas are as follows: (12) ； (13) ； (14) ； (15) ； (16) ； (17) ； (18) ； (19) ； (20) ； (21) ； In the formula, It is a 0-1 variable, representing the state when the task... On the belt conveyor If transportation is carried out, the value is 1; otherwise, it is 0. It is also a 0-1 variable, representing the state when the task... Select transportation route The value is 1 if the condition is met, and 0 otherwise. Represents a set of tasks. Indicates the task number; Represents the set of transportation routes. Indicates the transport route number; Indicates a collection of belt conveyors. Indicates the transportation route The collection of belt conveyors on the top is a subset of Indicates the belt conveyor number; This represents an extended task set, consisting of a real task set and a virtual termination task. composition; It is a 0-1 variable, representing the value on the belt conveyor. Virtual Startup Task Then came the actual transportation mission. The value is 1 if the condition is met, and 0 otherwise, indicating a real task. It is a belt conveyor The first transport object on the road; This represents an extended task set, consisting of the real task set and the virtual starting task. composition; It is a 0-1 variable, representing the belt conveyor. On real missions Then came the virtual transportation mission. The value is 1 if the condition is met, and 0 otherwise, indicating a real task. It is a belt conveyor The last transported item on the route; and All are 0-1 decision variables, used to represent the decision variables in the belt conveyor. On the task With the task The order of adjacent items in a belt conveyor On the task Immediately following the mission During execution, The value is 1 if it is not 1, and 0 otherwise; when on the belt conveyor On the task Immediately following the mission During execution, The value is 1 if it is set to 1, otherwise the value is 0. Indicates the start time of the task; Indicates task In the transportation route The start time of transport on the first conveyor belt; It is an infinitely large constant; Indicates task The earliest start time of the work; Indicates to perform a task Transportation routes The start-up time of the first belt conveyor; Indicates the transportation route The time required for the first belt conveyor to complete the startup operation; Indicates task On the belt conveyor The start time of transportation; Indicates to perform a task belt conveyor The boot time; Indicates belt conveyor Booting time; A 0-1 variable, representing the belt conveyor. In transportation mission Whether the boot process was previously executed; the value is 1 if the boot process is required, and 0 otherwise. and Representing tasks In its selected transportation route Upper Article and Section The start time of transport on the belt conveyor; Representing a path Upper The time required for a single belt conveyor to transport a unit quantity of goods; Indicates the transportation route The number of belt conveyors included above; S2-5: Establish the relevant constraints for the start-up, stop and dynamic switching logic of the belt conveyor, and characterize the start-up and stop operations of the belt conveyor between adjacent tasks; The constraints related to the belt conveyor start-stop and dynamic switching logic are given by providing the downtime of the belt conveyor after completing a task, the start-stop connection relationship between adjacent tasks, and the start-up and shutdown state conditions of the first and last tasks, so as to uniformly describe the time evolution process of the belt conveyor under two working conditions: continuous operation and shutdown and restart. Specifically, under the premise that the task has been assigned to a certain belt conveyor, formula (22) gives the lower bound of the downtime of the belt conveyor after completing the task based on the start time of the task on the belt conveyor, the unit transport time and the shutdown time. Formulas (23) and (24) are constrained by the task order variable. The start time of the next task is linked to the shutdown time of the previous task, so that the restart time window of the belt conveyor between the two tasks is consistent with whether the shutdown operation is performed; Formulas (25) and (26) use virtual start task and virtual end task to constrain the start state of the first task and the shutdown state of the last task of the belt conveyor with the work sequence; Formula (27) constrains the shutdown decision of the previous task with the start state variable of the next task between any pair of adjacent tasks, so that the two dynamic switching modes of continuous operation and shutdown restart are consistent at the variable level; (22) ； (23) ； (24) ； (25) ； (26) ； (27) ； In the formula, Indicates belt conveyor After completing the task The subsequent downtime; Indicates task On the belt conveyor The start time of transportation; Indicates the route taken by a unit quantity of goods. Required shipping time; Indicates belt conveyor Shutdown time; For 0-1 decision variables, when the task is completed Rear belt conveyor The value is 1 if an immediate shutdown operation is performed, and 0 otherwise. It is a 0-1 variable, representing the state when the task... On the belt conveyor If transportation is carried out, the value is 1; otherwise, it is 0. Indicates completion of task belt conveyor Startup time; For 0-1 decision variables, when on a belt conveyor On the task Immediately following the mission During execution, The value is 1 if it is set to 1, otherwise the value is 0. A variable of 0-1, representing the state during task execution. Front belt conveyor If the power-on process has been completed, the value is 1; otherwise, it is 0. It is a 0-1 variable, representing the value on the belt conveyor. Virtual Startup Task Then came the actual transportation mission. The value is 1 if the condition is met, and 0 otherwise, indicating a real task. It is a belt conveyor The first transport object on the road; It is a 0-1 variable, representing the belt conveyor. On real missions Then came the virtual transportation mission. The value is 1 if the condition is met, and 0 otherwise, indicating a real task. It is a belt conveyor The last transported item on the route; A variable of 0-1, representing the state during task execution. Front belt conveyor If the power-on process has been completed, the value is 1; otherwise, it is 0. S2-6: Based on the constraints related to the operation sequence and time continuity of the belt conveyor given in S2-4 and the constraints related to the start-up and dynamic switching logic of the belt conveyor given in S2-5, establish the constraints related to the number of belt conveyor starts and power limits; as shown in formulas (28) to (31): (28) ； (29) ； (30) ； (31) ； In the formula, Represents the set of discrete time steps. Indicates the number of the discrete time step; It is a 0-1 variable, when the task is completed. Transportation, belt conveyor At any moment The value is 1 if the process is in the startup phase, and 0 otherwise. This represents the upper limit of the number of belt conveyors allowed to be in the start-up state at any discrete moment, reflecting the capacity constraint of the terminal power system on the start-up load of belt conveyors; Indicates belt conveyor The boot time is an infinite constant. It is a 0-1 variable, representing the state when the task... On the belt conveyor If transportation is carried out, the value is 1; otherwise, it is 0. Indicates to perform a task belt conveyor The boot time; S2-7: Based on S2-1 to S2-6, establish domain constraints for model variables, and limit the range of values ​​for energy consumption variables, time variables and various 0-1 decision variables respectively, forming a complete variable space for the mixed integer programming model; The domain constraints of the model variables are shown in formulas (32) to (36): (32) ； (33) ； (34) ； (35) ； (36) ； In the formula, Indicates completion of task belt conveyor Energy consumption; Represents a set of tasks. and Indicates the task number; Indicates a collection of belt conveyors. Indicates the belt conveyor number; Indicates task Completion time; Indicates task On the belt conveyor The start time of transportation; Indicates to perform a task belt conveyor The boot time; Indicates belt conveyor After completing the task The subsequent downtime; Represents the set of transportation routes. Indicates the transport route number; Represents the set of discrete time steps. Indicates the number of the discrete time step; It is a 0-1 variable: Indicates when the task Select transportation route The value is 1 if it is 1, otherwise it is 0. Indicates when the task On the belt conveyor If transportation is carried out, the value is 1; otherwise, it is 0. Indicates when the task is completed Rear belt conveyor The value is 1 if an immediate shutdown operation is performed, and 0 otherwise. Indicates performing a task Front belt conveyor If the power-on process has been completed, the value is 1; otherwise, it is 0. Indicates when completing the task Transportation, belt conveyor At any moment The value is 1 if the process is in the startup phase, and 0 otherwise. Represents a stacked set. and Indicates the number of different stockpiles; Indicates a collection of material piles. Indicates the stockpile number; Indicates a stockpile The stacking station set on the top, Indicates the stack location number; It is a 0-1 variable: , indicating if stacking Assigned to stockpile And the leftmost stack position it occupies is The value is 1 if it is 1, otherwise it is 0. Indicates in the stockpile superior The occupied stack space is The value is 1 if it is on the left, otherwise it is 0; Indicates when in the stockpile superior Prior to The value is 1 when a stack space is occupied, and 0 otherwise. Represents the set of all extended tasks. , and These represent the virtual start task and the virtual end task, respectively. For 0-1 decision variables, when on a belt conveyor On the task Immediately following the mission During execution, The value is 1 if it is not 1, otherwise the value is 0.

6. The method according to claim 5 An energy-saving scheduling method for automated dry bulk cargo terminals considering dynamic switching of belt conveyors, characterized in that: Specifically, S3 is: S3-1: Instantiation and solution of the scheduling model; Based on the start and end times of the planning period and the arrival times of the tasks, the operation time axis is divided to determine the time set and time step. The information of stockpiles, stack locations, and stacks in the stockyard is mapped to the set and parameters related to stockyard location allocation. The transportation paths between tippers, ship loaders, and stockpiles, and the belt conveyor sequences on them, are mapped to the set and parameters related to task transportation path selection and belt conveyor allocation. The operation type, earliest start time, workload, and corresponding stacks of each task, as well as the unit time energy consumption, start-up time, shutdown time, unit cargo transportation time, and maximum number of simultaneous starts of each belt conveyor are written into the model parameters. On this basis, the weighted sum of the total task operation time and the total energy consumption of the belt conveyor system is used as the optimization objective. According to the objective function form and various constraint structures given in S2, a mixed integer programming framework consisting of a scheduling objective function and constraint conditions is constructed. Then, the above sets and parameters are substituted into the objective function and various constraint structures given in S2 to obtain the energy-saving scheduling optimization problem for the current planning period. The population size, maximum number of iterations, and convergence criteria required for the hybrid intelligent solution are set. S3-2: A hybrid intelligent solution strategy combining an improved genetic algorithm and mixed integer programming is adopted to jointly optimize the allocation of yard locations, selection of task transportation routes, and start-up and shutdown timing of belt conveyors; specifically: Using stacking location allocation, task transportation path selection, and belt conveyor start-stop control scheme as the coding structure, the discrete decision variables in S2 are represented by chromosomes to construct the initial population; The fitness function is used as the weighted sum of the total task operation time and the total energy consumption of the belt conveyor system in the S2 objective function to evaluate the individuals in the population. On this basis, selection, crossover and mutation operations are repeatedly performed to make the yard layout, path combination and start-up and shutdown strategy evolve in the feasible solution space. In each generation, the discrete decision structure of individuals with better fitness is fixed, and only continuous time variables such as task start time, belt conveyor start time, and downtime and energy consumption variables are retained. The mixed integer programming solution module is called to perform local fine optimization on the energy-saving scheduling optimization problem of S3-1 to obtain an improved solution under the current structure. The local optimization results are written back to the population and the fitness is updated. When the improvement of the optimal fitness is insufficient for several consecutive generations or the preset number of iterations is reached, the evolution process is terminated, and the individual with the smallest fitness is selected as the optimal scheduling solution that satisfies all S2 constraints. S3-3: Based on the optimal solution obtained in S3-2, generate a specific job scheduling scheme for the planning period; specifically: Based on the location of the stockyard, assign values ​​to relevant variables, determine the stockpile number and starting position number of each stockpile task, and give the spatial location of the stockpile in the stockyard. Based on the task transportation path, select the values ​​of relevant variables, determine the specific path of each stockpiling task and reclaiming task in the transportation path set, and determine the sequence of conveyor belts that each task passes through in combination with the conveyor belt sequence on the path. Based on the values ​​of time-related variables and belt conveyor start-up and dynamic switching variables, determine the start time, completion time, start transportation time on each belt conveyor for each task, as well as the start time, shutdown time, and switching method of each belt conveyor during the planning period, which involves maintaining continuous operation or performing shutdown and restart between adjacent tasks. By integrating the results of yard location allocation, transportation route selection, and conveyor start-stop timing, an energy-saving operation scheduling scheme for dry bulk cargo terminals is formed, covering the entire planning period. This scheme guides terminal operators in developing stockpiling plans, material handling plans, and conveyor start-stop control plans.

7. An automated dry bulk cargo terminal energy-saving scheduling system considering dynamic switching of belt conveyors, characterized in that, The automated dry bulk cargo terminal energy-saving scheduling system implements the automated dry bulk cargo terminal energy-saving scheduling method according to any one of claims 1-6, including a data acquisition module, a model building module, an optimization solution module, and a scheduling scheme generation module.

8. An automated dry bulk cargo terminal energy-saving scheduling system considering dynamic switching of belt conveyors as described in claim 7, characterized in that, The automated dry bulk terminal energy-saving scheduling system is specifically as follows: The data acquisition module is used to acquire yard characteristic data, stockpiling and reclaiming task information, and operating equipment parameter information from the automated dry bulk terminal site. The model building module is used to construct a hybrid integer programming model for energy-saving scheduling of dry bulk cargo terminals, taking into account the dynamic switching of belt conveyors, based on the input data provided by the data acquisition module. The optimization solution module is used to jointly optimize the yard location allocation, task transportation path selection, and belt conveyor start-up and shutdown sequence based on the mixed integer programming framework established by the model building module and a hybrid intelligent solution strategy. The scheduling scheme generation module is used to generate specific energy-saving operation scheduling schemes for the planning period based on the optimal or near-optimal solution results output by the optimization solution module.

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