Coal loading and unloading energy consumption dynamic optimization method and system based on digital twinning

Abstract

The application provides a coal loading and unloading energy consumption dynamic optimization method and system based on digital twinning, relates to the technical field of coal loading and unloading, and comprises the following steps: a digital twinning model containing a stockyard space structure, equipment movement range and business constraint rules is established; plan data, equipment state position data, stockyard resource occupation data and stockyard constraint data of coal bulk cargo loading and unloading operations are collected to form a data set; according to the data set and the digital twinning model, a coal bulk cargo loading and unloading scheme is determined, and when external conditions change, a scheme regeneration mechanism is triggered; after the coal bulk cargo loading and unloading scheme is completed, a first energy consumption value of all coal bulk cargo loading and unloading operations in the current period is calculated, a second energy consumption value corresponding to an off-peak time period in the current period is combined to obtain a comprehensive energy consumption index, and based on the comprehensive energy consumption index, energy-saving guidance information is provided for the next period. The application can realize energy saving and safe operation of coal loading and unloading operations.

Description

Technical Field

[0001] This application relates to the field of coal loading and unloading technology, and in particular to a method and system for dynamic optimization of coal loading and unloading energy consumption based on digital twins. Background Technology

[0002] Bulk coal loading and unloading is a crucial link in the coal logistics supply chain, primarily involving train unloading, yard storage, and ship loading. This process must adhere to a series of operational rules, such as matching coal types for storage and loading / unloading, prohibiting stackers and reclaimers from operating simultaneously at the same stack location, setting a maximum loading capacity for each stack (e.g., 27,000 tons), and prohibiting loading coal into empty stack locations. Simultaneously, energy conservation (e.g., prioritizing the smallest possible stack and minimizing equipment movement distance) and safety (e.g., collision prevention and stopping unloading under high-temperature conditions) must be considered. Therefore, achieving energy-efficient and safe operation in the coal loading and unloading process while meeting multiple constraints is a critical issue that urgently needs to be addressed within the industry.

[0003] In existing technologies, the formulation of bulk coal loading and unloading plans largely relies on manual experience or traditional dispatching systems. Manual dispatching requires combining information such as train arrival times, ship berthing dynamics, coal type demand, and equipment status to manually allocate storage locations and plan equipment operation paths and sequences. While traditional dispatching systems can automate the verification of some rules, they lack the ability to globally model the spatial structure of the storage yard, the range of equipment movement, and complex business constraints. In practice, plan formulation is often based on static data. When external conditions (such as changes in train forecasts, ship dynamic adjustments, and sudden equipment failures) change, it is difficult to respond quickly and generate optimal adjustment plans, and only local corrections can be made through manual intervention.

[0004] The main drawback of existing technologies lies in their inability to achieve dynamic optimization and global energy consumption control during the coal loading and unloading process. On the one hand, the lack of integrated modeling of yard resources, equipment status, and operational constraints easily leads to equipment occupancy conflicts during solution development. On the other hand, energy consumption calculations are mostly limited to the energy consumption of equipment operation during a single operation, failing to consider the standby energy consumption of equipment during off-peak periods, and making it difficult to systematically reduce energy consumption by optimizing equipment movement distances and operation sequences, thus failing to meet the industry's refined needs for energy conservation and consumption reduction. Furthermore, the lag and limitations in adjusting solutions in the face of changing external conditions further exacerbate the contradiction between efficiency and energy consumption, urgently requiring a technical solution that can dynamically adapt to changes in scenarios and globally optimize energy consumption. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for dynamic optimization of energy consumption in coal loading and unloading based on digital twins, so as to solve the problem that the existing technology cannot meet the industry's refined needs for energy conservation and consumption reduction.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a method for dynamic optimization of coal loading and unloading energy consumption based on digital twins, comprising:

[0007] Establish a digital twin model that includes the yard spatial structure, equipment movement range, and business constraint rules;

[0008] Collect planning data, equipment status and location data, yard resource occupancy data, and yard constraint data for bulk coal loading and unloading operations to form a data set. The yard resource occupancy data includes the status of empty stack spaces.

[0009] Based on the data set and the digital twin model, a coal bulk cargo loading and unloading scheme is determined. When external conditions change, a scheme regeneration mechanism is triggered. The coal bulk cargo loading and unloading scheme includes the allocation matrix between the ship's internal holds, coal type, and tonnage.

[0010] After the bulk coal loading and unloading plan is completed, the first energy consumption value of all bulk coal loading and unloading operations in the current cycle is calculated. Combined with the second energy consumption value corresponding to the off-peak time period in the current cycle, a comprehensive energy consumption index is obtained. Based on the comprehensive energy consumption index, energy-saving guidance information is provided for the next cycle.

[0011] Optionally, determining the bulk coal loading and unloading scheme based on the dataset and the digital twin model includes:

[0012] The dataset is dynamically aligned with the digital twin model to eliminate conflicting data in the dataset and generate a valid set of input parameters.

[0013] Based on the ship berthing window and train arrival time sequence in the set of valid input parameters, and combined with the equipment movement path topology in the digital twin model, the allowable execution time interval for coal bulk cargo loading and unloading tasks in the current period is calculated, and a task sequence mapping table with timestamps is generated.

[0014] Based on the coal type demand, flow direction attributes and stack tonnage constraints of the ship forecast in the data set, and combined with the preset operation mode, the spatial topological relationship between the stack and the ship's hold is simulated through a digital twin model. With the goal of minimizing the equipment movement distance, an allocation matrix between the ship's hold, coal type, stack position and tonnage is generated within the allowed execution time interval.

[0015] Based on the allocation matrix between ship hold, coal type, stack position and tonnage, determine the coal type loading sequence and ship hold loading sequence;

[0016] The task sequence mapping table, the allocation matrix, the coal type loading order, and the ship's hold loading order are input into the digital twin model. Combined with the real-time location status of the equipment, a coal bulk cargo loading and unloading scheme is generated in the form of a loading and unloading sequence Gantt chart.

[0017] Optionally, based on the coal type demand, flow direction attributes, and stack tonnage constraints predicted by ships in the dataset, and combined with a preset operating mode, a digital twin model is used to simulate the spatial topological relationship between the stack and the ship's hold. With the goal of minimizing equipment movement distance, an allocation matrix is ​​generated within the allowed execution time interval, comprising:

[0018] Based on the aforementioned flow direction attributes, the entire ship's cabins are divided into several independent work groups, which are either foreign trade isolation groups or domestic trade work groups.

[0019] By combining the preset operation mode, the three-dimensional coordinate data of the stack positions in the yard and the geometric center coordinates of the ship's cabins are called through the digital twin model to establish a distance mapping table from the center point of each stack position to the geometric center point of each cabin of the ship.

[0020] Based on the coal type requirements and stacking tonnage constraints, for each foreign trade isolation group, corresponding stacking positions are matched according to the coal type requirements. During the matching process, the tonnage allocation satisfies the sum of the ship hold capacities within the group. For the domestic trade operation group, based on the continuity relationship of adjacent ship holds, the same coal type is allocated to a set of ship holds with consecutive positions.

[0021] Based on the distance mapping table, for a single ship compartment operation group, the spatial straight-line distance between each ship compartment in the group and the stacking position assigned to each ship compartment is accumulated, with the goal of minimizing the accumulated distance value of all ship compartment operation groups, to generate an allocation scheme that meets the coal type requirements and tonnage constraints.

[0022] The allocation scheme is structured and output to obtain the allocation matrix.

[0023] Optionally, the step of outputting the allocation scheme in a structured manner to obtain an allocation matrix includes:

[0024] Based on the flow direction attributes of the ship's cabin, a unique group identifier is assigned to each independent work group;

[0025] The structured data is filled in layer by layer according to the group identifier. The first layer of the structured data is the sequence of ship compartment numbers arranged according to the physical compartment order of the ship. The second layer is the coal type name associated with each compartment number. The third layer is the tonnage. The upper limit of the total capacity of the ship compartments in the group and the constraint that empty stacks cannot be loaded with coal are checked simultaneously.

[0026] Load the stack position coordinate topology into the digital twin model, calculate the spatial straight line space of the stack positions allocated to adjacent cabins within the same work group, and if the spatial straight line distance exceeds the set distance threshold, exchange the stack position allocation relationship of non-adjacent cabins.

[0027] Based on all spatial straight-line distances and stack allocation relationships, an allocation matrix is ​​generated between ship holds, coal types, stack positions, and tonnage within the allowed execution time interval.

[0028] Optionally, determining the coal loading sequence and the ship's hold loading sequence based on the allocation matrix between the ship's hold, coal type, stack location, and tonnage includes:

[0029] Based on the volatile characteristics of the coal types and the allocation matrix, the coal types are prioritized to obtain the coal loading order.

[0030] The loading sequence of the ship's hold is determined based on the priority of the group identifier, the loading continuity characteristics within the group, the allocation matrix, and the operation mode.

[0031] Optionally, establishing a digital twin model that includes the yard spatial structure, equipment movement range, and business constraint rules includes:

[0032] The stockpile is raster-coded to obtain a reference grid coordinate system;

[0033] Map the boundary coordinates of the stack positions in the reference grid coordinate system to establish a stack position spatial topology database for characterizing the spatial structure of the stockyard;

[0034] Based on the equipment's mechanical parameters, the movement boundary is modeled to obtain the equipment's movement range;

[0035] The rules for foreign trade isolation, the rules for mixed coal risk, the constraints on equipment linkage, and the necessary conditions for unloading are combined into business constraint rules.

[0036] Based on the aforementioned yard spatial structure, equipment movement range, and business constraint rules, a digital twin model corresponding to the yard is constructed.

[0037] Optionally, the calculation of the first energy consumption value of coal loading and unloading operations for all bulk coal handling within the current period, combined with the second energy consumption value corresponding to the off-peak time period within the current period, yields a comprehensive energy consumption index, including:

[0038] For a single bulk coal loading and unloading operation, the single-machine loading and unloading energy consumption of each piece of equipment is calculated based on the operating status of each piece of equipment, including belt conveyors, stacker-reclaimers, monitoring equipment, and ship loaders.

[0039] The first energy consumption value for coal loading and unloading is obtained by summing up the single-machine loading and unloading energy consumption of all equipment in all bulk coal loading and unloading operations in the current period.

[0040] The second energy consumption value corresponding to the idle time period in the current cycle is determined based on the standby energy consumption of each device, the duration of the idle time period, and the number of idle time periods.

[0041] The first energy consumption value and the second energy consumption value are combined to obtain the comprehensive energy consumption index.

[0042] Secondly, this application provides a dynamic optimization system for coal loading and unloading energy consumption based on digital twins, including:

[0043] A module is established to create a digital twin model that includes the yard space structure, equipment movement range, and business constraint rules.

[0044] The data acquisition module is used to collect planned data, equipment status and location data, yard resource occupancy data, and yard constraint data for bulk coal loading and unloading operations, forming a data set.

[0045] The determination module is used to determine the bulk coal loading and unloading scheme based on the data set and the digital twin model. When external conditions change, the scheme is regenerated. The bulk coal loading and unloading scheme includes the correspondence between the ship's internal holds and the coal type, the correspondence between the ship's holds and the amount of coal, and the loading sequence of the coal type and the ship's holds.

[0046] The calculation module is used to calculate the first energy consumption value of coal loading and unloading for all coal bulk cargo loading and unloading operations in the current cycle after the coal bulk cargo loading and unloading plan is completed, obtain the comprehensive energy consumption index, and provide energy-saving guidance information for the next cycle based on the comprehensive energy consumption index.

[0047] Thirdly, this application provides an electronic device, comprising:

[0048] Memory, used to store computer programs;

[0049] A processor is configured to execute the computer program to implement the steps of the dynamic optimization method for coal loading and unloading energy consumption based on digital twins as described in the first aspect above.

[0050] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the dynamic optimization method for coal loading and unloading energy consumption based on digital twins as described in the first aspect above.

[0051] The method for dynamic optimization of coal loading and unloading energy consumption based on digital twins provided in this application includes: establishing a digital twin model that includes the spatial structure of the stockyard, the range of equipment movement, and business constraint rules; collecting planned data, equipment status and location data, stockyard resource occupancy data, and stockyard constraint data of bulk coal loading and unloading operations to form a data set, wherein the stockyard resource occupancy data includes the status of empty stacks; determining a bulk coal loading and unloading scheme based on the data set and the digital twin model, and triggering a scheme regeneration mechanism when external conditions change, wherein the bulk coal loading and unloading scheme includes the allocation matrix between ship holds, coal type, and tonnage; after the bulk coal loading and unloading scheme is completed, calculating the first energy consumption value of coal loading and unloading for all bulk coal loading and unloading operations in the current cycle, and combining it with the second energy consumption value corresponding to the idle time period in the current cycle to obtain a comprehensive energy consumption index, and providing energy-saving guidance information for the next cycle based on the comprehensive energy consumption index.

[0052] The method provided in this application has the following advantages:

[0053] (1) By incorporating necessary unloading conditions into the digital twin model (such as matching coal types for storage, stackers and reclaimers not being allowed to operate simultaneously at the same stack, a maximum coal loading limit of 27,000 tons, empty stacks not being allowed for loading, and corresponding stacks being unusable when equipment malfunctions), the generated loading and unloading scheme is ensured to strictly adhere to business rules, preventing violations from the outset and guaranteeing the standardization and stability of the work process. The digital twin model can incorporate safety-related constraints (such as collision prevention requirements requiring a one-stack interval between stacking and reclaiming, and no unloading at temperatures above 40 degrees Celsius), and through simulation verification, ensure that the scheme meets safety standards, reduce risks such as equipment collisions and high-temperature operations, and provide technical assurance for operational safety.

[0054] (2) Based on the planned data (such as the train arrival time), a time-stamped task sequence is generated, and combined with the mechanism of triggering the scheme to regenerate the scheme by changes in external conditions, the goal of "ensuring that all trains operate at the same time as much as possible, reducing equipment occupation conflicts and waiting time" is achieved. At the same time, the scheme is optimized with "shortest total operation time" as the core indicator, which significantly improves the overall efficiency of unloading and subsequent operations.

[0055] (3) On the one hand, by simulating the movement path of equipment through digital twin model, the optimization direction is to "minimize the movement distance of the stacker" and "prioritize the smallest stack". This directly reduces the energy consumption of equipment operation. On the other hand, by calculating the "first energy consumption value of all operations in the current cycle" and the "second energy consumption value of the idle time period", a comprehensive energy consumption index is obtained to provide energy-saving guidance for the next cycle, forming a closed loop of energy consumption control and systematically reducing the energy consumption of loading and unloading process.

[0056] (4) Supports the selection of operation mode (stock preparation mode, balance mode, high production mode) based on the actual situation of the yard, and flexibly responds to the priority requirements of different modes through digital twin model (such as stock preparation mode giving priority to stacks with small yard capacity, and high production mode giving priority to shortening unloading time), which improves the adaptability of the solution in complex scenarios.

[0057] (5) The design of the allocation matrix of ship cabins, coal types and tonnage can be directly adapted to the energy-saving and efficiency requirements of the loading process, such as realizing "the smaller the moving distance of the material reclaimer, the better" and "prioritizing the selection of stacks with smaller stack numbers to shorten the belt distance", and recommending loading modes in combination with ship cabin information, promoting the coordinated optimization of unloading and loading processes, and improving the overall efficiency of the logistics chain.

[0058] (6) By dynamically processing data and combining the timing of ships and trains with the topology of equipment paths, a task sequence is generated. The spatial topology is simulated and an allocation matrix is ​​generated with the goal of minimizing the equipment movement distance. This can meet the necessary conditions such as coal type matching and tonnage constraints in unloading operations, and meet the requirements of energy saving (shortening the equipment movement distance) and production scheduling according to arrival time, thereby improving loading and unloading efficiency and energy saving effect. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 A flowchart illustrating a dynamic optimization method for coal loading and unloading energy consumption based on digital twins, provided in an embodiment of this application;

[0061] Figure 2 A schematic diagram illustrating an application scenario of a dynamic optimization method for coal loading and unloading energy consumption based on digital twins, provided in an embodiment of this application.

[0062] Figure 3 This is a schematic diagram of a dynamic optimization system for coal loading and unloading energy consumption based on digital twins, provided in an embodiment of this application. Detailed Implementation

[0063] To address the inability of existing technologies to meet the industry's refined demands for energy conservation and consumption reduction, this application provides a dynamic optimization method for coal loading and unloading energy consumption based on digital twins. This method employs the following concept: by constructing a digital twin model encompassing the yard structure, equipment scope, and business rules, it integrates data such as work plans, equipment status, and yard resources to generate loading and unloading schemes that include allocation of ship holds, coal types, and tonnage. These schemes can be automatically updated when external conditions change. Simultaneously, by calculating a comprehensive index of operational energy consumption and idle energy consumption, it provides energy-saving guidance for subsequent cycles. This solution leverages digital twins to achieve global visualization and dynamic optimization, resolving issues of equipment conflicts and adjustment lags in traditional methods. Furthermore, it achieves systemic energy conservation through full-cycle energy consumption management, effectively balancing operational efficiency and energy costs.

[0064] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0065] The core of this application is to provide a dynamic optimization method for coal loading and unloading energy consumption based on digital twins. A flowchart of one specific implementation method is shown below. Figure 1 As shown, the method includes:

[0066] S11. Establish a digital twin model that includes the yard space structure, equipment movement range, and business constraint rules.

[0067] In S11, the spatial structure of the stockpile refers to the physical layout information of the stockpile, including the distribution, boundaries, and relative positions of the stacks; the equipment movement range refers to the movement boundaries of mobile equipment such as stackers and reclaimers, such as the track range and the radius of motion of robotic arms; business constraint rules are a set of rules to ensure compliance, safety, and efficiency of operations, including foreign trade isolation rules (i.e., physical isolation between foreign and domestic trade goods), coal mixing risk rules (i.e., different coal types cannot be mixed), equipment linkage constraints (i.e., logical restrictions on collaborative operations between equipment), and necessary conditions for unloading, such as matching coal types for storage, a maximum stacking capacity of 27,000 tons, and no coal loading in empty stacks; the digital twin model is a virtual replica of the real stockpile and operating environment, which can map the physical entity status in real time and support simulation optimization.

[0068] The digital twin model corresponds to the storage yard. For example... Figure 2As shown, in an exemplary stockpile, four tippers can be installed, namely CD1, CD2, CD3, and CD4, which begin operation after the train arrives. Four belt conveyors (hereinafter referred to as belts) are also installed below the tipper hoppers, namely BF1, BF2, BF3, and BF4, for transporting coal. Each belt is equipped with a belt scale corresponding to the tipper, coded BSBF1, BSBF2, BSBF3, and BSBF4, respectively, used to obtain relevant electronic scale information for the corresponding tipper. This electronic scale information includes instantaneous values, cumulative values, and the number of tipper sections. For safe transportation, this embodiment of the application provides belt conveyor transfer towers at different belt positions: T4, T5, T6, T7, T8, T9, T10, T11, etc.; BH1-3, BH1-4, BH2-3, BH2-4, BH3-3, BH3-4, BH4-3, BH4-4 represent the codes of the intermediate belts of different turning lines in the corresponding longitudinal direction. There are 3 intermediate belts for the loading and unloading lines: BJ1-1, BJ2-1, and BJ3-1; MSBQ1, MSBQ2, and MSBQ3 represent the corresponding electromagnetic separators. There are 4 stacker belts: BD1, BD2, BD3, and BD4. There are 4 reclaimer belts: BQ1, BQ2, BQ3, and BQ4. There are 3 ship loaders: SL1, SL2, and SL3; and 3 conveyor belts connecting the ship loaders: BM1, BM2, and BM3. This application includes four stacker cranes in the stockyard, namely S1, S2, S3, and S4. It also includes multiple reclaimers in the stockyard: R1-1, R1-2, R2-1, R2-2, R3-1, and R3-2. For example, the stockyard can have 6 rows and 4 columns, thus the number of stockyard positions can be 24, such as stockyard position 101, stockyard position 102, stockyard position 103, ..., stockyard position 604, etc. There are multiple types of coal, such as coal type A, coal type B, coal type C, coal type D, and coal type E.

[0069] In the embodiments of this application, the establishment of the digital twin model can be achieved through the following steps 111 to 115, which will not be repeated here.

[0070] S12. Collect planned data, equipment status and location data, yard resource occupancy data and yard constraint data for bulk coal loading and unloading operations to form a data set. The yard resource occupancy data includes the status of empty stacks.

[0071] In S12, the planning data includes train arrival plans (train number, coal type, estimated arrival time) and ship berthing plans (ship name, voyage number, coal type demand, berthing / departure time), etc.; equipment status and location data can refer to the operating status (working / idle / faulty) and real-time location of equipment such as stackers and reclaimers, such as grid coordinates; yard resource occupancy data includes the occupancy status of each stack (occupied / empty stack), coal type and tonnage; yard constraint data refers to fixed conditions that restrict operations, such as a maximum stacking capacity of 27,000 tons, no unloading above 40°C, etc.; the dataset is a unified dataset that integrates the above types of data, providing basic information for scheme formulation.

[0072] In this embodiment, the following data are collected through the port's information management system, equipment sensors, and manual input: train arrival plans (e.g., train D carrying coal type E, expected arrival at 8:00 AM); ship berthing plans (e.g., ship F carrying voyage 1, requiring 5000 tons of coal type E, berthing at 9:00 AM); equipment status and location data (e.g., position of stacker M, grid coordinates (20, 30), status (idle)); and reclaimer N's ​​position (40, 50), status (operating); yard resource occupancy data (e.g., stack A (occupied, coal type E, 20000 tons) and stack B (empty stack)) are collected through the yard monitoring system; and yard constraint data (e.g., maximum stack capacity 27000 tons, high-temperature non-unloading) are also recorded. Finally, these data are integrated to form a complete dataset.

[0073] S13. Based on the data set and digital twin model, determine the bulk coal loading and unloading scheme. When external conditions change, trigger the scheme regeneration mechanism. The bulk coal loading and unloading scheme includes the allocation matrix between the ship's internal holds and the coal type and tonnage.

[0074] In S13, changes in external conditions can refer to changes in train forecast information, ship forecast information, ship berthing and departure times, train estimated arrival times, and ship loading information. Train forecast information includes train number, coal type, and tonnage; ship forecast information includes ship name, voyage number, coal type, tonnage, flow direction, and number of holds; and ship loading information refers to loading methods, which holds hold which coal types, the tonnage loaded, and the loading sequence. The allocation matrix is ​​structured data recording the correspondence between holds, coal types, stack positions, and tonnage.

[0075] In the embodiments of this application, the generation of the bulk coal loading and unloading scheme can be achieved through the following steps 131 to 135, which will not be described in detail here.

[0076] S14. After the bulk coal loading and unloading plan is completed, calculate the first energy consumption value of all bulk coal loading and unloading operations in the current cycle, and combine it with the second energy consumption value corresponding to the off-peak time period in the current cycle to obtain the comprehensive energy consumption index. Based on the comprehensive energy consumption index, provide energy-saving guidance information for the next cycle.

[0077] In S14, the first energy consumption value is the total operating energy consumption of all equipment (belt conveyors, stacker-reclaimers, etc.) during the current cycle; the second energy consumption value is the total standby energy consumption of the equipment during idle periods (times when there is no work) during the current cycle; the comprehensive energy consumption index is the sum of the first and second energy consumption values, used to reflect the total energy consumption level within the cycle. Energy-saving guidance information includes: the amount of work, the process (equipment string) used, the flow rate used, and ultimately, achieving the most energy-efficient operation. It should be noted that since the comprehensive energy consumption index for this cycle can provide energy-saving guidance information for the next cycle, the comprehensive energy consumption index for the previous cycle can also provide energy-saving guidance information for the current cycle.

[0078] In the embodiments of this application, the generation of the comprehensive energy consumption index can be achieved through the following steps 141 to 144, which will not be described in detail here.

[0079] In a specific example, a coal terminal plans to establish a digital twin model. First, the stockpile is divided into a 10m×10m grid, with each grid coordinate represented by (X, Y). For example, (10, 20) represents the 10th column and 20th row grid. Then, in the coordinate system, stack position A (boundary coordinates X1-X3, Y1-Y3), stack position B (boundary coordinates X4-X6, Y4-Y6), etc., are marked, and stack positions A and B are recorded as adjacent. Then, based on the 500m track length of the stacker M, its movement range is defined in the model as X0-X500, with fixed Y values. At the same time, rules such as "coal type C can only be stored in stack positions marked C" and "the stacker and reclaimer cannot enter the same stack position area at the same time" are included. Finally, a complete digital twin model is formed, which can reflect the stockpile layout, equipment location, and rule constraints in real time. A coal terminal collects data before operations: Planning data shows train G (coal type H, 3000 tons, expected arrival at 10:00) and ship I (voyage 2, requiring 4000 tons of coal type H, berthing at 11:00); Equipment status and location data shows stacker J is idle at (15, 25) and reclaimer K is operating at (35, 45); Yard resource occupancy data shows stack C (occupied, coal type H, 25000 tons) and stack D (empty stack); Yard constraints include a stack limit of 27000 tons and no unloading above 40℃. This data is integrated into a dataset for subsequent planning. Based on the model in S11 and the dataset in S12, conflicting data was found during processing where "stacking position D was simultaneously assigned to two trains." This conflict was removed, and valid parameters were generated. Based on Ship I's berthing time of 11:00-15:00 and Train G's arrival time of 10:00, a task sequence was generated: 10:00-10:30 Train G unloads to stall D; 11:00-14:00 Loads Ship I from stall D. Since Ship I is a domestic trade vessel, its three holds are divided into a domestic trade group. The coordinates of stall D and holds 1-3 are used, and the distances are calculated to be 100m, 120m, and 110m respectively. Stacking position D is matched according to coal type H, allocating 1500 tons to hold 1, 1500 tons to hold 2, and 1000 tons to hold 3 (total 4000 tons, not exceeding the stall limit), and optimized to the order of hold 1→3→2 (shorter total distance). Finally, a Gantt chart is generated to display the time schedule for each task. If train G is delayed, the system automatically recalculates the time interval and updates the plan. Within a certain period, two operations are completed: unloading train G and loading ship I. During unloading, the conveyor belt runs for 0.5 hours (power 50kW), consuming 25kWh of energy, and the stacker J runs for 0.5 hours (power 60kW), consuming 30kWh of energy. During loading, the reclaimer K runs for 3 hours (power 40kW), consuming 120kWh of energy, and the conveyor belt runs for 3 hours (power 50kW), consuming 150kWh of energy. The first energy consumption value is 25+30+120+150=325kWh.The downtime within the cycle is from unloading to loading (0.5 hours). Stacker J (5kW) consumes 2.5kWh of standby energy, and reclaimer K (6kW) consumes 3kWh of standby energy, resulting in a second energy consumption of 5.5kWh. The total energy consumption is 325 + 5.5 = 330.5kWh. Analysis shows that the downtime can be reduced, guiding the optimization of operational connections in the next cycle to decrease standby energy consumption.

[0080] The digital twin-based dynamic optimization method for coal loading and unloading energy consumption provided in this application achieves digital integration of yard space, equipment movement, and business rules by constructing a digital twin model. This provides a virtual simulation environment for the formulation of subsequent operation plans, allowing for the simulation of the operation process in advance to avoid conflicts and ensuring the compliance and safety of operations. By collecting and integrating various types of data, it provides comprehensive and accurate basic information for the formulation of operation plans, ensuring that the plans fit actual operating conditions and avoiding unreasonable plans due to incomplete information. By dynamically processing data, simulating spatial relationships, and optimizing the operation sequence, it generates efficient loading and unloading plans that meet constraints and can adjust in real time in response to changes in external conditions, reducing equipment conflicts and waiting time, and improving operational efficiency and flexibility. By calculating the comprehensive index of operational energy consumption and idle standby energy consumption, it achieves comprehensive control over energy consumption, providing data basis for energy-saving optimization in the next cycle, helping to systematically reduce energy consumption and improve the economic efficiency of operations.

[0081] In some optional embodiments, S11, establishing a digital twin model including the yard space structure, equipment movement range, and business constraint rules, including:

[0082] Step 111: Perform grid encoding on the stockpile to obtain the reference grid coordinate system.

[0083] In step 111, the raster coding process is the process of dividing the entire stockyard area into several regular grids according to a fixed size and assigning a unique identifier (such as coordinate value) to each grid. The reference grid coordinate system is a coordinate system formed by raster coding and used to accurately locate any position in the stockyard. The coordinates of each grid can uniquely correspond to the physical area in the actual stockyard, providing a basis for the subsequent digitization of spatial information.

[0084] In this embodiment, firstly, the size of the grid is determined (e.g., a 5m×5m or 10m×10m grid is selected based on the actual size of the storage yard and the required operational accuracy); then, a two-dimensional coordinate system is established with a fixed point in the storage yard (e.g., the northwest corner vertex) as the origin, with the horizontal axis as the X-axis and the vertical axis as the Y-axis; next, each grid is assigned a unique coordinate (e.g., (X1, Y1)(X1, Y2)...(Xn, Yn)) in the order from left to right and from top to bottom, where X represents the horizontal grid number and Y represents the vertical grid number; finally, a reference grid coordinate system covering the entire storage yard is formed, realizing the digital segmentation of the storage yard space. For example, a storage yard is 500m long and 300m wide. If a grid size of 10m×10m is selected and the northwest corner is taken as the origin, a grid of 50 columns (X=1 to 50) and 30 rows (Y=1 to 30) can be divided. The grid (10, 15) corresponds to an area of ​​100-110m in the X direction and 150-160m in the Y direction in reality.

[0085] Step 112: Map the boundary coordinates of the stack positions in the reference grid coordinate system to establish a stack position spatial topology database for characterizing the spatial structure of the storage yard.

[0086] In step 112, the boundary coordinates of the stack position refer to the grid range occupied by each stack position in the reference grid coordinate system, which is usually represented by the minimum and maximum grid coordinates (e.g., the boundary of stack position A is X1-X3, Y1-Y3); the stack position spatial topology relation database is a database that records the spatial relationships between all stack positions (e.g., adjacent, contained, separated, etc.), which is used to intuitively reflect the layout structure of the stack positions in the yard.

[0087] In this embodiment, in the reference grid coordinate system obtained in step 111, the grid range corresponding to the physical boundary of each stack position is determined, and the minimum X coordinate, maximum X coordinate, minimum Y coordinate, and maximum Y coordinate (i.e., boundary coordinates) of each stack position are recorded. Then, by comparing the boundary coordinates of each stack position, the spatial relationship between them is determined—if the boundary coordinates of two stack positions are adjacent grids (e.g., the maximum X coordinate of stack position A + 1 equals the minimum X coordinate of stack position B), they are determined to be adjacent; if the boundary coordinates of stack position C are completely contained within the boundary coordinates of stack position D, they are determined to be contained; finally, the boundary coordinates of all stack positions and their mutual topological relationships are organized and stored in the database to form a stack position spatial topological relationship database. For example, in the grid system of step 111, the boundary coordinates of stack position M are X5-X8, Y10-Y13, and the boundary coordinates of stack position N are X9-X12, Y10-Y13. By comparison, it is found that the two are adjacent in the X direction, so "stack position M and stack position N are adjacent" is recorded in the topological relationship database.

[0088] Step 113: Based on the mechanical parameters of the equipment, model the moving boundary to obtain the moving range of the equipment.

[0089] In step 113, the equipment mechanical parameters refer to the physical performance parameters of mobile devices such as stackers and reclaimers, including track length, maximum operating radius of the robotic arm, moving speed limit, and turning angle limit; moving boundary modeling is the process of defining the spatial boundary of the equipment's movement in the reference grid coordinate system based on the equipment mechanical parameters; the equipment movement range refers to the set of all grid areas that the equipment can reach during operation, used to limit the equipment's activity area and avoid exceeding the physical range, including the constraint that stackers and reclaimers cannot operate simultaneously in the same stack position.

[0090] In this embodiment, firstly, mechanical parameters of equipment such as stackers and reclaimers are collected, such as the track laying range of the stacker (X-direction from Xa to Xb, Y-direction fixed at Yc) and the maximum working radius R of the reclaimer's robotic arm. Then, in the reference grid coordinate system obtained in step 111, the movement boundary of the equipment is delineated according to these parameters—for track-type equipment, its movement range is the grid area covered by the track (e.g., Xa-Xb, Yc); for rotary equipment, a circular or fan-shaped movement area is delineated in the grid system with its fixed base coordinates (X0, Y0) as the center and the working radius R as the radius. Finally, the delineated boundary is converted into a specific grid coordinate range to obtain the equipment's movement range. For example, if the track of stacker B is laid at position X5-X50, Y10, according to the track length parameter, its movement range in the reference grid is all grids corresponding to X5-X50, Y10.

[0091] Step 114: Combine the foreign trade isolation rules, coal mixing risk rules, equipment linkage constraints, and necessary unloading conditions into business constraint rules.

[0092] In step 114, the foreign trade isolation rule refers to the rule that foreign trade coal and domestic trade coal must be physically separated for storage or handling to prevent cross-contamination; the coal mixing risk rule refers to the rule that different types of coal (such as thermal coal and coking coal) cannot be mixed for storage or loading and unloading to avoid affecting coal quality; the equipment linkage constraint refers to the logical restrictions that must be followed when multiple pieces of equipment work together (such as stackers and reclaimers cannot enter the same stack area at the same time to reduce equipment occupation conflicts, and anti-collision constraints such as stacking and reclaiming being separated by one stack); the necessary conditions for unloading refer to the prerequisites that must be met for unloading operations (such as the coal type matching the coal type marked on the stack, the stack not reaching the maximum capacity of 27,000 tons, coal can only be loaded in non-empty stacks, and unloading is not allowed when the temperature is above 40 degrees Celsius); the business constraint rule is a collection of the above four types of rules, used to standardize the operation process and ensure compliance and safety.

[0093] In this embodiment, the specific requirements of the foreign trade isolation rules are first clarified, such as "there must be at least two grids between foreign trade coal stacks and domestic trade coal stacks"; then, the details of the coal mixing risk rules are clarified, such as "coal type E cannot be stored in areas marked as coal type D"; next, the content of equipment linkage constraints is determined, such as "the stacker and reclaimer cannot enter grid areas X10-X20 and Y5-Y15 at the same time"; then, the necessary conditions for unloading are organized, such as "the coal type unloaded must be consistent with the coal type marked on the target stack", "the current tonnage stored in the stack + the tonnage to be unloaded ≤ 27,000 tons", and "empty stacks cannot be used for unloading"; finally, these rules are classified and integrated to form a complete set of business constraint rules, which serve as the compliance judgment standard for subsequent scheme formulation. For example, the integrated rules include isolation requirements such as "foreign trade coal must be stored in areas X1-X20 and domestic trade coal must be stored in areas X30-X50", as well as coal mixing restrictions such as "coal type F and coal type G cannot be mixed in the same stack".

[0094] Step 115: Based on the yard spatial structure, equipment movement range, and business constraint rules, construct a digital twin model corresponding to the yard.

[0095] In step 115, the yard space structure refers to the yard layout information reflected by the stack space topology relation library established in step 112; the equipment movement range is the grid area where each piece of equipment can move, obtained in step 113; the business constraint rules are the various operation rules integrated in step 114; the digital twin model is a digital model that integrates the above three types of information into the virtual environment and forms a one-to-one correspondence with the real yard, which can map the status of the real yard (such as stack occupancy and equipment position) in real time and support operation simulation.

[0096] In this embodiment, firstly, the topological relationship library of the stack space obtained in step 112 is imported into the virtual modeling platform to reconstruct the spatial layout of the yard, including the position, boundaries, and interrelationships of each stack. Then, the equipment movement range obtained in step 113 is loaded into the model, and the movable areas of each piece of equipment are marked in the virtual yard. Next, the business constraint rules integrated in step 114 are converted into logic code recognizable by the model (e.g., rule verification is achieved through conditional statements) and embedded into the virtual model. Finally, real-time data synchronization between the virtual model and the real yard is achieved through a data interface (e.g., real-time acquisition of equipment position and stack occupancy status), enabling the virtual model to dynamically reflect the real-world state and completing the construction of the digital twin model. For example, the positions and adjacency relationships of stacks A1, A2, and A3 in step 112 are first reconstructed in the virtual platform, then the movement ranges of stacker B and reclaimer C in step 113 are marked, and rules such as foreign trade isolation in step 114 are embedded, ultimately forming a real-time updatable digital twin model of terminal A.

[0097] In a specific example, the storage yard of coal terminal A has an area of ​​600m × 400m. To achieve spatial digitization, an 8m × 8m grid size is used for raster encoding. With the southwest corner of the storage yard as the origin, the horizontal (east-west) direction is the X-axis, and the vertical (north-south) direction is the Y-axis, resulting in a grid of 75 columns (X=1 to 75) and 50 rows (Y=1 to 50). The coordinates of each grid are represented by (X, Y). Grid (20, 30) corresponds to an area of ​​152-160m in the X direction and 232-240m in the Y direction in reality. This coordinate system allows for precise location of equipment or storage positions within this area. Based on the grid system of coal terminal A in step 111, boundary coordinate mapping is performed on the 30 stacking positions within the yard: the boundary of stacking position A1 is X10-X15, Y20-Y25; the boundary of stacking position A2 is X16-X21, Y20-Y25; and the boundary of stacking position A3 is X10-X15, Y26-Y31. By comparing the boundary coordinates, it is found that A1 and A2 are adjacent in the X direction, A1 and A3 are adjacent in the Y direction, and A2 and A3 are neither adjacent nor contained within each other. This information (including the boundary coordinates and interrelationships of each stacking position) is entered into the system to form the stacking position spatial topology database of terminal A. Coal terminal A has two main mobile equipment: stacker B and reclaimer C. Stacker B is a track-mounted type, with the track laid along the X direction, ranging from X8 to X60 and Y15 (fixed). Based on its mechanical parameters, after modeling the moving boundary, its moving range is all the grids corresponding to X8-X60 and Y15 in the base grid. Reclaimer C is a rotary type, with its base located at grid (30, 25). The maximum operating radius of the robotic arm is 20m (corresponding to 2.5 8m grids). Therefore, its moving range is a circular area centered at (30, 25) with a radius of 2.5 grids, covering the grids from (28, 23) to (32, 27). These ranges are recorded in the system as spatial constraints for equipment operation. The business constraint rules for Coal Terminal A are integrated as follows: The foreign trade isolation rule stipulates that "foreign trade coal can only be stored in grid areas X1-X25 and Y1-Y50, while domestic trade coal can only be stored in areas X30-X75 and Y1-Y50, with a buffer zone of X26-X29 reserved between the two types of areas"; the mixed coal risk rule stipulates that "high-calorific-value coal and low-calorific-value coal cannot be stored in adjacent stacks"; the equipment linkage constraint stipulates that "the operating areas of stacker B and reclaimer C cannot overlap"; the necessary conditions for unloading include "the type of coal unloaded must be consistent with the coal type marked on the stack," "the current tonnage of the stack + the tonnage unloaded ≤ 27,000 tons," and "empty stacks (without marked coal types) cannot be unloaded." These rules together constitute the business constraint rule system of Terminal A.Coal terminal A constructs a digital twin model in a virtual modeling platform: First, the topological relationship library of the stacking positions from step 112 is imported to recreate the positions and adjacency relationships of 30 stacking positions in the virtual environment; then, the movement ranges of stacker B (X8-X60, Y15) and reclaimer C (from (28, 23) to (32, 27)) from step 113 are loaded and marked in the virtual yard with different colors; next, the business constraint rules from step 114 are converted into logic code (e.g., when the system detects that foreign trade coal is attempting to be stored in the domestic trade area, a violation alert is automatically triggered); finally, the coal type, tonnage, and equipment location data of the actual stacking positions are obtained in real time through sensor interfaces and synchronized to the virtual model. The resulting digital twin model can intuitively display the yard status and simulate the operation process.

[0098] By executing steps 111 to 115 above, this embodiment of the application transforms the physical stockpile into a calculable digital space through a reference grid coordinate system formed by raster encoding processing. This provides a unified spatial reference standard for subsequent stack location and equipment movement path planning, ensuring the accuracy and consistency of spatial information. By establishing a stack spatial topology database, the distribution and mutual positional relationships of stacks in the stockpile are clearly presented, providing an intuitive spatial reference for subsequent equipment path planning and stack allocation. This helps avoid inefficiencies or safety issues caused by spatial conflicts during equipment operation. By modeling the movement boundary based on mechanical parameters, the... The digital twin model defines the movable range of each piece of equipment, ensuring that its movement within the model aligns with physical reality. This prevents plan failures due to mismatches between the virtual model and actual equipment performance, providing reasonable spatial constraints for subsequent operational path planning. By combining various rules to form business constraint rules, the model clarifies the compliance and safety requirements that must be followed during operations, providing clear judgment criteria for subsequent plan development. This prevents violations from the outset and ensures the orderly conduct of operations. The digital twin model achieves the digital integration of the yard's spatial structure, equipment movement range, and business constraint rules, forming a virtual mirror consistent with the real-world yard. This model not only reflects the real-time state but also supports operational simulation and optimization, providing a reliable virtual environment for subsequent loading and unloading plan development, conflict detection, and dynamic adjustments, thus improving the precision of operational management.

[0099] In some optional embodiments, S13, based on the dataset and digital twin model, a coal bulk cargo loading and unloading scheme is determined, including:

[0100] Step 131: Dynamically align the dataset with the digital twin model to eliminate conflicting data in the dataset and generate a valid set of input parameters.

[0101] In step 131, dynamic alignment refers to the process of comparing the information in the dataset with the spatial structure, equipment scope, and business constraint rules in the digital twin model in real time to ensure that the data is consistent with the model logic. Conflicting data refers to information in the dataset that does not conform to the model rules or is contradictory, such as the same stacking position being assigned to two trains at the same time, or the planned unloading tonnage exceeding the maximum capacity of the stacking position. The effective input parameter set refers to the dataset that conforms to the model rules and is logically consistent after removing data conflicts (such as the same stacking position being assigned to two trains at the same time), providing reliable input for subsequent scheme formulation.

[0102] In this embodiment, firstly, the data set generated in step 12 is imported into the digital twin model via a data interface; then, the model calls built-in business constraint rules (such as the upper limit of stack capacity and coal type matching requirements) and spatial topology relationships (such as the location of stacks) to verify each piece of information in the data set—for example, checking whether "Train C plans to unload coal to stack A" conforms to rules such as "the coal type of stack A is consistent with the coal type of Train C" and "the current remaining capacity of stack A is ≥ the unloading tonnage of Train C"; if conflicting data is found (such as the unloading tonnage of Train C exceeding the remaining capacity of stack A), the data is marked and removed; finally, all data that passes the verification is organized into a valid input parameter set. For example, if there is a record in the data set that "Train D plans to unload 5000 tons of coal to stack B (remaining capacity 3000 tons)," a tonnage conflict is found through dynamic alignment, and the record is removed, the remaining data forms a valid input parameter set.

[0103] Step 132: Based on the ship berthing window period and train arrival time sequence in the effective input parameter set, and combined with the equipment movement path topology in the digital twin model, calculate the allowable execution time interval for coal bulk cargo loading and unloading tasks in the current period, and generate a task sequence mapping table with timestamps.

[0104] In step 132, the ship berthing window period refers to the time range from berthing to departure (e.g., 10:00-14:00), which is the time constraint for loading tasks; the train arrival sequence refers to the expected order in which multiple trains arrive at the port (e.g., train E arrives at 9:00, train H arrives at 9:30); the equipment movement path topology refers to the route and distance relationship of equipment (e.g., stacker, reclaimer) from one location to another, recorded in the digital twin model; the allowable execution time interval refers to the time range within which a single task (e.g., unloading, loading) can begin and end, which must meet conditions such as equipment availability and seamless connection between tasks; the timestamped task sequence mapping table is a table that records each task (e.g., "train E unloading", "ship I loading") and its allowable execution time interval in chronological order. The loading and unloading sequence Gantt chart is a chart that visually displays the time arrangement of each operation task, and may include constraints to ensure that all trains operate simultaneously to reduce equipment occupation conflicts; changes in external conditions include changes in train arrival times, dynamic adjustments of ships, equipment failures, etc.

[0105] In this embodiment, firstly, the ship berthing window (e.g., ship I 10:00-14:00) and train arrival times (e.g., train E 9:00, train H 9:30) are extracted from the set of valid input parameters. Then, the topology of the equipment movement paths in the digital twin model is invoked to calculate the movement time for each task (e.g., it takes 10 minutes for the stacker to move from the unloading point of train E to the stacking position G). Next, combining the equipment movement time and the task's own operation time (e.g., it takes 1 hour for train E to unload 6000 tons), the allowable execution time interval for each task is calculated—for example, unloading of train E can be carried out between 9:00 and 10:00 (arrival at 9:00, 1 hour of operation, 10 minutes to move to the next position, without affecting subsequent tasks). Finally, all tasks and their time intervals are arranged in chronological order to generate a timestamped task sequence mapping table. For example, the mapping table records "9:00-10:00 train E unloading to stacking position G" and "10:00-13:00 reclaimer loading ship I from stacking position G".

[0106] Step 133: Based on the coal type demand, flow direction attributes and stack tonnage constraints of the ship forecast in the dataset, and combined with the preset operation mode, simulate the spatial topology relationship between the stack and the ship's hold through a digital twin model. With the goal of minimizing the equipment movement distance, generate the allocation matrix between the ship's hold, coal type, stack position and tonnage within the allowed execution time interval.

[0107] In step 133, during coal unloading operations, the preset operation modes include: preparation mode, balancing mode, and high-yield mode. Coal type demand refers to the type of coal that the ship needs to load (e.g., coal type F); flow direction attribute refers to whether the coal transported by the ship is for domestic or foreign trade, which determines the division of operation groups; stack tonnage constraint refers to the maximum storage capacity of the stack (e.g., 27,000 tons) and the current remaining capacity; the preset operation modes include preparation mode (prioritizing smaller capacity stacks), balancing mode (evenly distributing cargo volume), and high-yield mode (prioritizing shortening time); spatial topology relationship refers to the relative positional relationship between the stack and the ship's hold; the allocation matrix is ​​a structured table that records the correspondence between "ship's hold - coal type - stack - tonnage", for example, "ship's hold 1 - coal type F - stack G - 5,000 tons".

[0108] Specifically, step 133 can be implemented through the following process, for example, including: Step a1, dividing all the ship's holds into several independent work groups according to the flow direction attribute, the independent work groups being either foreign trade isolation groups or domestic trade work groups; Step a2, combining the preset work mode, using the digital twin model to call the three-dimensional coordinate data of the stack positions in the yard and the geometric center coordinates of the ship's holds, establishing a distance mapping table from the center point of each stack position to the geometric center point of each ship's holds; Step a3, according to the coal type requirements and stack position tonnage constraints, for each foreign trade isolation group, according to the coal type requirements... The matching process involves matching the required coal type with the corresponding storage location. During the matching process, the tonnage allocation satisfies the sum of the ship hold capacities within the group. For domestic trade operation groups, based on the continuity relationship between adjacent ship holds, coal types are allocated to a set of ship holds with consecutive locations. Step a4: Based on the distance mapping table, for a single ship hold operation group, the spatial straight-line distance between each ship hold within the group and the storage location allocated to each ship hold is accumulated. The goal is to minimize the accumulated distance value of all ship hold operation groups to generate an allocation scheme that satisfies the coal type requirements and tonnage constraints. Step a5: The allocation scheme is output in a structured manner to obtain the allocation matrix.

[0109] In steps a1 to a5 above, the flow direction attribute refers to the intended use of coal transportation, divided into foreign trade (for export) and domestic trade (for domestic sales). An independent work group is a set of ship holds divided according to the flow direction attribute. Ship holds within the same group must follow the same operating rules. The foreign trade isolation group is required to be physically isolated from the domestic trade work group, while the domestic trade work group can arrange operations according to the principle of continuity. Preset operating modes include high-yield mode (prioritizing shorter operating time) and energy-saving mode (prioritizing reduced equipment movement), which affect the weight of distance calculation. Three-dimensional coordinate data refers to the spatial position (X, Y, Z) of the stack location in the digital twin model, where Z is the stacking height. The geometric center coordinates of the ship holds refer to the spatial position (X, Y, Z) of the center point of each ship hold on the vessel. The distance mapping table is a table recording the straight-line distance between the stack location and the ship hold, used for subsequent optimization allocation. Coal type requirement refers to the type of coal that the work group needs to load (e.g., coal type F); stack position tonnage constraints include the maximum capacity of the stack position (e.g., 27,000 tons) and the current remaining capacity (maximum capacity - tonnage already stored); the sum of the ship hold capacities within the group refers to the total tonnage that can be loaded in all ship holds of the same work group; the continuity relationship of adjacent ship holds refers to the requirement that adjacent ship holds in the domestic trade work group (e.g., numbered 1 and 2) must maintain loading continuity (same coal type, same stack position source). The cumulative distance value refers to the sum of the movement distance of all stackers within a single work group; minimizing the cumulative distance value is an optimization objective, which reduces the total movement distance of equipment during operation by rationally allocating the correspondence between stack positions and ship holds; the allocation scheme refers to the preliminary scheme that clarifies which stack position corresponds to each ship hold and how many tons are allocated. Structured output refers to organizing the allocation scheme into a standardized data format according to a fixed hierarchy (such as group, ship hold, coal type, tonnage); the allocation matrix is ​​the result of the structured output, which is a table containing the correspondence between "group identifier - ship hold number - coal type - stack position - tonnage", used to clearly show the allocation logic.

[0110] For example, firstly, the ship's holds are divided into independent work groups (export isolation group or domestic trade work group) according to the flow direction attribute; then, combined with the preset work mode (such as high-yield mode), the three-dimensional coordinates of the stack positions are called through the digital twin model, such as the center point of stack position G (X1, Y1, Z1) and the geometric center coordinates of the ship's holds, such as the center point of ship's holds 1 (X2, Y2, Z2), and a distance mapping table between the two is calculated and established; next, according to the coal type demand (such as coal type F) and the tonnage constraint of the stack positions (such as stack position G having 5000 tons remaining), the corresponding stack positions are matched for each work group—the export group is strictly matched according to the coal type, and the domestic trade group will allocate the same coal type to consecutive ship holds; then, based on the distance mapping table, the distance between the ship holds and the corresponding stack positions within the group is accumulated, and the allocation scheme is optimized with the goal of minimizing the total distance; finally, a unique identifier is assigned to the work group, and the ship hold number, coal type, and tonnage are filled in layer by layer. After verifying the constraints, the stack position allocation of adjacent ship holds is adjusted (if the distance exceeds the threshold, they are swapped), and an allocation matrix is ​​generated. For example, the three holds of domestic trade vessel I are divided into one work group. The coordinates of stack positions G (5000 tons remaining) and J (4000 tons remaining) and holds 1-3 are called up. After calculating the distance, the "hold 1 - stack position G - 3000 tons", "hold 2 - stack position G - 2000 tons", and "hold 3 - stack position J - 4000 tons" are assigned, with the total distance being the minimum.

[0111] Step a5, which involves outputting the allocation scheme in a structured format to obtain an allocation matrix, may include the following steps: Step a51, assigning a unique group identifier to each independent work group based on the flow direction attribute of the ship's hold; Step a52, filling in the structured data layer by layer according to the group identifier. The first layer of the structured data is the sequence of ship hold numbers arranged according to the order of the ship's physical hold positions; the second layer is the coal type name associated with each ship hold number; and the third layer is the tonnage. The upper limit of the total capacity of the ship holds within the group and the constraint that empty stacks cannot be loaded with coal are simultaneously verified; Step a53, loading the stack coordinate topology into the digital twin model, calculating the spatial straight-line space of the stacks allocated to adjacent ship holds within the same work group, and exchanging the stack allocation relationship of non-adjacent ship holds if the spatial straight-line distance exceeds the set distance threshold; Step a54, generating an allocation matrix between ship holds, coal type, stack position, and tonnage based on all spatial straight-line distances and stack allocation relationships within the allowed execution time interval.

[0112] In the above embodiment, for step a1, firstly, the ship's flow direction attribute is extracted from the dataset (e.g., determined by ship customs declaration information as foreign trade or domestic trade); then, it is checked whether all ship holds need to be split according to the flow direction attribute—if the ship only transports coal in a single flow direction (e.g., pure domestic trade), then all ship holds are divided into an independent operating group (e.g., domestic trade operating group); if the ship transports both foreign trade and domestic trade coal (e.g., the front half of the ship holds is loaded with foreign trade coal, and the rear half with domestic trade coal), then the ship holds are split into a foreign trade isolation group and a domestic trade operating group according to the flow direction attribute, with physical isolation space reserved between the two groups (e.g., at least one empty hold between them); finally, the ship hold numbers contained in each group are recorded to complete the division of independent operating groups. For example, if ship I is a pure domestic trade ship and contains 3 ship holds (numbered 1, 2, and 3), then it is divided into 1 domestic trade operating group, containing ship holds 1-3. For step a2, based on the preset operating mode (e.g., energy-saving mode requires prioritizing equipment movement distance), the priority of distance calculation is determined. Then, through the interface of the digital twin model, the three-dimensional coordinates of the stack locations within the stockpile that meet the coal type requirements (e.g., the center point of stack location G (10, 20, 5), Z being the current stack height) and the geometric center coordinates of each ship's hold (e.g., the center point of hold 1 (30, 40, 8)) are called. Next, the distance from each stack location to each ship's hold is calculated using the spatial straight-line distance formula, where the distance calculation formula is: Where (X1, Y1, Z1) are the coordinates of the center point of the stack, and (X2, Y2, Z2) are the coordinates of the geometric center of the cabin; finally, all the correspondences of "stack position - cabin - distance" are organized into a distance mapping table. For example, in the preset energy-saving mode, the distance from stack position G to cabin 1 is calculated to be 25m, and the distance to cabin 2 is calculated to be 27m, forming a mapping table. For step a3, extract the coal type demand for each work group (e.g., foreign trade isolation group 01 requires coal type H) and the sum of the ship hold capacities within the group (e.g., 10,000 tons). Then, for the foreign trade isolation group: select stacking positions from the digital twin model that match the coal type (coal type H) and have remaining capacity ≥ the sum of the ship hold capacities within the group. If the capacity of a single stacking position is insufficient, match multiple stacking positions of the same coal type to ensure that the total remaining capacity ≥ the required tonnage. For the domestic trade work group: after selecting stacking positions that match the coal type, prioritize allocating the same coal type to ship holds with consecutive locations (e.g., ship holds 1-3), and the sum of the remaining capacity of the stacking positions must ≥ the sum of the ship hold capacities within the group. Finally, record the matched stacking positions and the range of tonnage that can be allocated. For example, the domestic trade work group requires 8,000 tons of coal type F, matching stacking positions G (5,000 tons remaining) and J (4,000 tons remaining), with a total remaining capacity of 9,000 tons ≥ 8,000 tons, satisfying the constraint. For step a4, extract the distance data between all ship holds and matching stacks within a single work group from the distance mapping table. Then, using a greedy algorithm (prioritizing the allocation of the closest stack to the ship hold) or an enumeration method, try different "ship hold-stack" matching combinations and calculate the cumulative distance value for each combination. Next, among all combinations that meet the coal type requirements (e.g., coal type matching) and tonnage constraints (e.g., remaining stack capacity ≥ allocated tonnage), select the combination with the smallest cumulative distance value. Finally, based on this combination, determine the corresponding stack and allocated tonnage for each ship hold (the total tonnage within the group must equal the sum of the ship hold capacities), generating an allocation scheme. For example, if a domestic trade work group has 2 ship holds and 2 stacks, by calculating the cumulative distance of the two matching combinations, select the scheme with the smaller total distance (e.g., ship hold 1-stack J, ship hold 2-stack G, total distance 45m). For step a51, firstly, count the number of independent work groups of the vessels and their respective flow direction attributes (foreign trade or domestic trade); then, classify and number them according to flow direction attributes—foreign trade work groups are prefixed with "foreign trade", and domestic trade work groups are prefixed with "domestic trade", with the serial number starting from 01 and increasing sequentially (e.g., the first foreign trade group is "foreign trade 01", the second is "foreign trade 02"); finally, associate the group identifier with the cabin number contained in the work group to ensure that each work group has a unique identifier.For example, a ship has two foreign trade groups and one domestic trade group, which are respectively assigned "Foreign Trade 01", "Foreign Trade 02", and "Domestic Trade 01" to ensure differentiation between groups. Then, for step a52, each work group is traversed by group identifier, and the ship hold numbers contained in the group are filled in the first layer of the structured data (arranged in the physical order of the ship, such as 1→2→3). Then, the coal type name corresponding to each ship hold is filled in the second layer (it must be consistent with the coal type matched in step a3). Next, the allocated tonnage of each ship hold is filled in the third layer (it must satisfy that the capacity of a single ship hold ≤ allocated tonnage ≤ remaining capacity of the stacking position). At the same time, it is verified that "the sum of the tonnage of all ship holds in the group = the upper limit of the total capacity of the ship holds in the group" and "all allocated stacking positions are non-empty stacking positions (coal types are marked)". If they are not satisfied, the tonnage or stacking position is adjusted. Finally, the structured data that passes the verification is recorded. For example, in the domestic trade group 01, the first layer is filled with 1→2→3, the second layer is filled with coal type F, and the third layer is filled with 3000→2000→2000. The total tonnage is verified to be 7000 tons (equal to the total capacity within the group) and the stacking positions are not empty. Next, for step a53, the three-dimensional coordinates of the stacking positions allocated to each hold within the same work group are loaded into the digital twin model. Then, the spatial straight-line distance between the stacking positions corresponding to adjacent holds (such as holds 1 and 2) is calculated (using the distance formula in step a2). Next, the calculation result is compared with the set distance threshold (such as 50m). If it exceeds the threshold (such as 60m), non-adjacent holds within the group (such as holds 1 and 3) are searched, and their stacking position allocation relationship is swapped. The stacking position distance between adjacent holds is calculated again until the stacking position distance between all adjacent holds is ≤ the threshold. Finally, the adjusted stacking position allocation relationship is recorded. For example, if the stacking distance between adjacent holds 1 (stack position A) and 2 (stack position B) is 70m (exceeding the threshold of 50m), swap the stacking positions of holds 1 and 3 (hold 1 → stack position C, hold 3 → stack position A). After adjustment, the stacking distance between holds 1 and 2 is 40m (≤ the threshold). Finally, for step a54, integrate the stacking allocation relationship adjusted in step a53 (e.g., hold 1 → stack position J), coal type information (e.g., coal type F), tonnage (e.g., 4000 tons), and group identifier (e.g., domestic trade 01). Then, associate the allowed execution time interval of the work group determined in step 132 (e.g., 10:00-13:00). Next, organize the data in the order of "group identifier → hold number → coal type → stack position → tonnage → time interval" to form a standardized table format. Finally, check whether the data in the matrix is ​​complete and whether the logic is consistent (e.g., whether the total tonnage is correct). After confirming that there are no errors, output the allocation matrix. For example, the allocation matrix of the domestic trade 01 group contains multiple rows of data similar to "domestic trade 01, 1, coal type F, stack position J, 4000 tons, 10:00-13:00".

[0113] Through steps a51 to a54 above, by assigning unique group identifiers, clear distinction and efficient management of different work groups are achieved, providing a convenient identification basis for subsequent data filling, verification, and work tracking. By filling data layer by layer and simultaneously verifying constraints, the accuracy and compliance of structured data are ensured, laying the data foundation for generating the final allocation matrix. By adjusting the stacking position allocation of adjacent holds, the movement distance of equipment when working between adjacent holds is ensured to be within a reasonable range, reducing equipment adjustment time and energy consumption, and improving work continuity. The generated allocation matrix completely records the key information required for the operation, clarifying the correspondence between "hold-coal type-stacking position-tonnage" and associating it with time constraints, providing comprehensive data support for subsequently determining the loading sequence and generating Gantt charts.

[0114] Through steps a1-a5 above, by dividing independent work groups according to flow direction attributes, the foreign trade and domestic trade work areas were strictly distinguished, meeting the business constraints of foreign trade isolation and laying the foundation for subsequent allocation of stacking positions and work arrangement by group, ensuring the compliance of the work. By establishing a distance mapping table, the spatial relationship between stacking positions and ship holds was quantified, providing a data basis for subsequent allocation optimization with the goal of "minimizing equipment movement distance" and adapting to the needs of the preset work mode. By matching stacking positions according to coal type and tonnage constraints, it was ensured that the coal type requirements and loading volume of the work groups met the requirements. At the same time, the continuous allocation of the domestic trade group reduced the number of times equipment was adjusted between different ship holds, improving work efficiency. By optimizing the allocation scheme with the goal of minimizing cumulative distance, the total movement distance of equipment during operation was reduced while meeting the coal type and tonnage constraints, reducing energy consumption and improving the economy of the operation. The allocation matrix completely records the key information required for the operation.

[0115] Step 134: Based on the allocation matrix between ship holds, coal types, stack positions, and tonnage, determine the coal loading sequence and ship hold loading sequence.

[0116] In step 134, the volatile matter characteristic value of coal refers to the ability of coal to release volatiles after heating (e.g., high volatile coal is prone to spontaneous combustion), which affects the loading priority; the coal loading sequence refers to the loading order of different coal types (e.g., high volatile coal is prioritized); the priority of the group identifier refers to the order of operation of different work groups (e.g., foreign trade group, domestic trade group); the loading continuity characteristic within the group refers to the loading connection requirements of adjacent holds within the same work group (e.g., continuous loading reduces equipment movement); the hold loading sequence refers to the loading order of each hold within the same work group.

[0117] Specifically, step 134 may include the following process: Step b1, prioritize the coal types according to their volatile matter characteristics and allocation matrix to obtain the coal loading order; Step b2, determine the loading order of the ship's hold according to the priority of the group identifier, the loading continuity characteristics within the group, the allocation matrix, and the operation mode.

[0118] In steps b1-b2, the operating mode, such as high-production mode, prioritizes unloading time. The volatile matter characteristic value of coal refers to the proportion of volatile matter released when heated (e.g., high volatile coal > 30%, medium volatile coal 10%-30%, low volatile coal < 10%). Higher volatile matter content makes it more prone to spontaneous combustion and requires priority loading. The coal loading order refers to the order in which different coal types are loaded (e.g., high volatile coal is loaded first). The priority of group identifiers refers to the order in which different operating groups are operated (e.g., foreign trade group prioritizes domestic trade group). The continuous loading characteristic within a group means that adjacent holds within the same operating group must be loaded continuously (reducing equipment movement). The operating mode (e.g., high-production mode) affects the sorting logic (e.g., prioritizing efficiency). The hold loading order refers to the order in which holds within the same operating group are operated.

[0119] In this embodiment, firstly, all relevant coal types (such as coal types F and H) are extracted from the allocation matrix; then, the volatile matter characteristics (VOCs) of each coal type are queried, such as coal type F being 20% ​​(medium) and coal type H being 35% (high); next, the VOCs are sorted from high to low (high > medium > low), and if the VOCs are the same, they are sorted by coal type name or work group priority; finally, the coal loading order is determined. For example, if the allocation matrix contains coal types H (high VOCs) and F (medium VOCs), the loading order is H→F. First, the order of work groups is determined by the priority of group identifiers (e.g., Foreign Trade 01 → Domestic Trade 01). Then, for each work group, based on the stack location distribution in the allocation matrix (e.g., holds 1-3 correspond to the same stack location) and the continuity within the group, priority is given to sorting by the physical hold location order (1→2→3). If holds within a work group correspond to multiple stack locations, and following the physical order would result in frequent equipment movement across stack locations (e.g., 1→3→2), the sorting is adjusted to follow the stack location concentration principle (e.g., load all holds in the same stack location first, then load the next stack location). Finally, the final hold loading order is determined by considering the preset work mode (e.g., high-production mode prioritizes shortening time). For example, holds 1-3 in the Domestic Trade 01 group all correspond to stack location G, so they are loaded in the order 1→2→3.

[0120] Through the above steps b1~b2, the loading sequence of coal types is determined according to the volatile characteristic value, and high volatile coals that are easy to spontaneously combust are processed first, which reduces the safety risks of coal during stacking and transportation and ensures the safety of operations. By comprehensively considering the group priority, continuity and operation mode to determine the loading sequence of the ship's hold, important operation groups are given priority processing, and the movement of equipment between different ship holds and stacks is reduced, which improves the efficiency and continuity of operations.

[0121] Step 135: Input the task sequence mapping table, allocation matrix, coal type loading sequence and ship hold loading sequence into the digital twin model, and combine it with the real-time location status of the equipment to generate a coal bulk cargo loading and unloading plan displayed in the form of a loading and unloading sequence Gantt chart.

[0122] In step 135, the real-time location status of the equipment refers to the current position (such as grid coordinates) of equipment such as stackers and reclaimers in the digital twin model; the loading and unloading sequence Gantt chart is a visual chart that uses time as the horizontal axis and tasks as the vertical axis to intuitively display the start time, end time, equipment allocation and connection relationship of each task, and clearly present the time arrangement of the entire loading and unloading plan.

[0123] In this embodiment, the task sequence mapping table (including task time intervals) of step 132, the allocation matrix (including the correspondence between ship hold, stack position, and tonnage) of step 133, and the coal type and ship hold loading sequence of step 134 are input into the digital twin model. Then, the model calls the real-time position status of the equipment (e.g., the reclaimer is currently at stack position K and needs to move to stack position G) to simulate the execution process of each task—for example, adjusting the task start time according to the equipment movement time to ensure the connection between tasks. Finally, the simulation results are converted into a loading and unloading sequence Gantt chart. The chart uses different colors to mark tasks such as unloading and loading, clearly indicating the time range, equipment used, and corresponding stack position / ship hold for each task. For example, information such as "9:00-10:00 Stacker M-Train E unloads to stack position G", "10:00-10:10 Reclaimer N moves from stack position K to stack position G", and "10:10-11:10 Reclaimer N-loads into ship hold 1 (stack position J)" are clearly visible in the Gantt chart.

[0124] In a specific example, the digital twin model of coal terminal A has been constructed through steps 111-115: the stockpile uses an 8m×8m grid encoding, and the baseline grid coordinate system covers a 600m×400m area; the stacking position spatial topology database records the boundary coordinates and adjacency relationships of 30 stacking positions (e.g., stacking positions G and J are adjacent); the equipment movement range clarifies the movable areas of stacker B (tracks X8-X60, Y15) and reclaimer C (operating radius 20m); business constraint rules include "export coal and domestic coal are separated by 4 grids", "coal types in the same stacking position cannot be mixed", and "maximum stacking position capacity is 27,000 tons", etc. Based on this model, the specific process of steps 131-135 is as follows:

[0125] The dataset for coal terminal A includes: train E (coal type F, 5000 tons, expected arrival at 9:00 AM, planned unloading to racking position G), train H (coal type F, 4000 tons, expected arrival at 9:30 AM, planned unloading to racking position G), and ship I (requiring coal type F, 9000 tons, berthing at 10:00 AM). When dynamically aligning the dataset with the digital twin model, the model calls the tonnage constraint of racking position G (maximum capacity 8000 tons, currently 3000 tons stored, 5000 tons remaining). It finds that "train E plans to unload 5000 tons to G" meets the constraint (3000 + 5000 = 8000), but "train H plans to unload 4000 tons to G" would result in overcapacity (8000 + 4000 = 12000 > 8000), thus identifying it as conflicting data. After removing the original plan for H, a set of valid input parameters is generated (the plan for train E is retained, and train H needs to be reassigned to stack J with coal type F and remaining capacity ≥ 4000 tons).

[0126] Based on the valid input parameter set, the berthing window for ship I is 10:00-13:00 (requiring 3 hours to complete the loading of 9000 tons), train E (5000 tons) arrives at 9:00, and train H (4000 tons, assigned to stacking position J) arrives at 9:30. The digital twin model calls the topology of the equipment movement paths: it takes 10 minutes for the stacker to move from train E to stacking position G, and 15 minutes from train H to stacking position J; the unloading efficiency is 1000 tons / 10 minutes (5000 tons takes 50 minutes, and 4000 tons takes 40 minutes). Calculate the allowed execution time intervals: Train E unloading 9:00-9:50 (arrival at port at 9:00, 10 minutes for movement, 50 minutes for operation); Train H unloading 9:30-10:25 (arrival at port at 9:30, 15 minutes for movement, 40 minutes for operation); Ship I loading 10:00-13:00 (reclaimer picks up material from G and J, completing 9000 tons in 3 hours). The generated task sequence mapping table records the above tasks and intervals according to time.

[0127] Taking ship J as an example: Ship I is a domestic trade ship, requiring 9,000 tons of coal of type F, with 3 holds (numbered 1-3). Step a1 divides it into "Domestic Trade Operation Group 01"; Step a2 calls the coordinates of stack position G (coordinates (10, 20, 5), remaining 5,000 tons), stack position J (coordinates (15, 20, 5), remaining 4,000 tons) and holds 1 ((30, 40, 8)), 2 ((32, 40, 8)), and 3 ((34, 40, 8)) to calculate the distance mapping table (G to 1≈28.3m, G to 2≈30.1m, G to 3≈31.9m; ​​J to 1≈25.2m, J to 2≈27.1m, J to 3≈29.0m); Step a3 matches G and J ( All are coal type F, with a total remaining supply of 9000 tons (demand). Step a4 aims to minimize the cumulative distance by selecting "Holding 1-J (4000 tons), Holding 2-G (3000 tons), Holding 3-G (2000 tons)" (total distance 25.2 + 30.1 + 31.9 = 87.2 m). Step a5 generates the allocation matrix: "Domestic Trade 01, 1, F, J, 4000, 10:00-13:00", "Domestic Trade 01, 2, F, G, 3000, 10:00-13:00", "Domestic Trade 01, 3, F, G, 2000, 10:00-13:00".

[0128] Ship J transports both foreign trade coal and domestic trade coal, with 5 holds (1-5): holds 1-2 carrying foreign trade coal are designated as "Foreign Trade Isolation Group 01", holds 4-5 carrying domestic trade coal are designated as "Domestic Trade Operation Group 01", and hold 3 is an isolated empty hold; the final matrix is ​​formed in the order of "group identifier, hold number, coal type, stack position, tonnage, and time interval", including: "Foreign Trade 01, 1, H, K, 3000, 14:00-17:00", "Foreign Trade 01, 2, H, L, 3000, 14:00-17:00", "Domestic Trade 01, 4, F, M, 3000, 14:00-16:00" and "Domestic Trade 01, 5, F, Q, 2000, 14:00-16:00".

[0129] Coal type H has a volatile matter content of 35% (high), and coal type F has a volatile matter content of 20% (medium). Based on the volatile matter content from highest to lowest, the loading order for coal types is H→F. The priority for the work groups on vessel J is Foreign Trade 01→Domestic Trade 01. Foreign Trade 01 group is loaded in the order of holds 1→2 (within the same stack K), and Domestic Trade 01 group is loaded in the order of 4→5 (to minimize equipment movement). For vessel I, Domestic Trade 01 group is loaded in the order of 1→2→3 (priority given to consecutive holds and stacks). The final loading order for the holds is: Vessel J 1→2→Vessel I 1→2→3→Vessel J 4→5.

[0130] Input the task sequence mapping table, allocation matrix, and loading order into the digital twin model, and combine it with the real-time location of the equipment (stacker M is next to train E, reclaimer N is at stack position K) to generate a Gantt chart: the horizontal axis is 9:00-17:00, and the vertical axis records the tasks in sequence such as "9:00-9:50 stacker M - unload to train E to G", "9:30-10:25 stacker P - unload to train H to J", "10:00-10:10 reclaimer N moves to J", "10:10-11:10 reclaimer N - loads ship I, hold 1", "11:10-12:10 loads ship I, hold 2", "12:10-13:00 loads ship I, hold 3", "13:00-13:30 reclaimer N moves to K", "13:30-15:00 loads ship J, holds 1-2", etc., to intuitively display the entire process.

[0131] Through steps 131-135 above, this application can achieve dynamic data verification and conflict elimination based on the digital twin model, ensuring the accuracy and compliance of input parameters. On this basis, by combining the ship berthing window, train arrival time, and equipment movement path, a scientific task sequence and time interval are generated, avoiding task conflicts and waiting. By simulating spatial topology and optimizing the allocation matrix with the goal of minimizing equipment movement distance, and by combining coal type characteristics and operation mode to determine a reasonable loading sequence, the complete solution is presented intuitively in the form of a Gantt chart. This not only meets business constraints such as foreign trade isolation, coal type matching, and tonnage restrictions, but also reduces equipment movement energy consumption and operation downtime, improving the efficiency, safety, and energy saving of coal loading and unloading. At the same time, it provides a basis for flexible adjustment in response to changes in external conditions, realizing intelligent and refined management of the entire operation process.

[0132] In some optional embodiments, in S14, the first energy consumption value of coal loading and unloading for all bulk coal loading and unloading operations in the current cycle is calculated, and combined with the second energy consumption value corresponding to the off-peak time period in the current cycle, a comprehensive energy consumption index is obtained, including:

[0133] Step 141: For a single bulk coal loading and unloading operation, calculate the single-machine loading and unloading energy consumption of each piece of equipment based on the operating status of each piece of equipment. The equipment includes belt conveyors, stacker-reclaimers, monitoring equipment, and ship loaders.

[0134] In step 141, a single bulk coal loading and unloading operation refers to a complete unloading, stacking, or loading operation (such as unloading train E to stacking position G); the operating status of the equipment includes running time, power, etc. (such as the belt conveyor running at 50kW power for 0.5 hours); the energy consumption of a single loading and unloading machine refers to the energy consumed by a single piece of equipment in a single operation (such as the electricity consumed by a stacker-reclaimer in a certain loading operation), which is calculated as the product of the equipment power and the running time.

[0135] In this embodiment, firstly, for a single operation (such as unloading train E to stacking position G), the equipment involved in the operation (belt conveyor A, stacker B) is recorded; then, the operating parameters of each piece of equipment are obtained—the power of belt conveyor A is 50kW, and the operating time is 0.5 hours; the power of stacker B is 60kW, and the operating time is 0.5 hours; next, the energy consumption of a single machine for loading and unloading is calculated according to "energy consumption = power × time", that is, the energy consumption of belt conveyor A = 50 × 0.5 = 25kWh, and the energy consumption of stacker B = 60 × 0.5 = 30kWh; finally, the energy consumption of a single machine for loading and unloading is recorded. For example, in a ship loading operation, the reclaimer C operates at 40kW power for 3 hours, and the energy consumption is 40 × 3 = 120kWh; the belt conveyor D operates at 50kW power for 3 hours, and the energy consumption is 50 × 3 = 150kWh.

[0136] Step 142: Accumulate the single-machine loading and unloading energy consumption of all equipment in all bulk coal loading and unloading operations within the current period to obtain the first energy consumption value for coal loading and unloading.

[0137] In step 142, the current period refers to the set statistical time period (such as one day or one shift); all bulk coal loading and unloading operations include all unloading and loading operations within the period; the first energy consumption value is the total energy consumed by all equipment during the operation within the period, reflecting the actual energy consumption level of the operation.

[0138] In this embodiment, firstly, the current cycle (e.g., 9:00-13:00) is determined; then, the energy consumption of each individual equipment for loading and unloading during all single operations within the cycle is collected—for example, in the unloading operation of train E, the energy consumption of belt E is 37.35 kWh and the energy consumption of stacker F is 58.1 kWh; in the unloading operation of train H, the energy consumption of belt G is 29.8 kWh and the energy consumption of stacker H is 45.5 kWh; in the loading operation of ship I, the energy consumption of reclaimer I is 120 kWh and the energy consumption of belt J is 150 kWh; next, these energy consumption values ​​are added together: 37.35 + 58.1 + 29.8 + 45.5 + 120 + 150 = 440.75 kWh; finally, the accumulated result is determined as the first energy consumption value.

[0139] Step 143: Determine the second energy consumption value corresponding to the idle time period in the current cycle based on the standby energy consumption, idle time period length and number of idle time periods of each device.

[0140] In step 143, standby energy consumption refers to the energy consumption per unit time when the equipment is not in operation but is in the power-on state (e.g., the standby power of a stacker is 5kW); idle time period refers to the idle time during which there is no operation within the cycle (e.g., the interval between two operations), including waiting time caused by equipment failure or operation conflict; time length refers to the duration of a single idle time period (e.g., 0.5 hours); number of idle time periods refers to the number of idle times within the cycle; the second energy consumption value is the sum of the standby energy consumption of all equipment during idle time within the cycle.

[0141] In this embodiment, firstly, two idle time periods within the current cycle are recorded (e.g., 9:50-10:00 and 11:10-11:20), each with a duration of 0.17 hours. Then, the standby power of each device is obtained: the stacker's standby power is 5kW, the reclaimer's standby power is 6kW, and the conveyor belt's standby power is 2kW. Next, the total standby energy consumption for a single idle time period is calculated: (5+6+2)×0.17≈2.21kWh. Multiplying this by the number of idle time periods (twice), the total standby energy consumption is 2.21×2≈4.42kWh. Finally, the result is determined as the second energy consumption value. For example, with three idle time periods (each 0.5 hours), the total standby power of the devices is 10kW, and the second energy consumption value is 10×0.5×3=15kWh.

[0142] Step 144: Combine the first energy consumption value and the second energy consumption value to obtain the comprehensive energy consumption index.

[0143] In step 144, the comprehensive energy consumption index is the sum of the first energy consumption value (operational energy consumption) and the second energy consumption value (standby energy consumption), which reflects the total energy consumption of coal loading and unloading in the current cycle and is used to assess the overall energy consumption level and guide subsequent energy-saving optimization.

[0144] In this embodiment, firstly, the first energy consumption value (e.g., 440.75 kWh) obtained in step 142 and the second energy consumption value (e.g., 4.42 kWh) obtained in step 143 are obtained; then, the two are added together: 440.75 + 4.42 ≈ 445.17 kWh; finally, the result is determined as the comprehensive energy consumption index for the current period. For example, if the first energy consumption value is 350 kWh and the second energy consumption value is 15 kWh, the comprehensive energy consumption index = 350 + 15 = 365 kWh.

[0145] For example, in a single operation of unloading train E to stacking position G, the participating equipment is conveyor belt E (power 45kW, running for 50 minutes) and stacker F (power 70kW, running for 50 minutes). When calculating the energy consumption of a single machine for loading and unloading, first convert the running time to hours: 50 minutes = 50 / 60 ≈ 0.83 hours; then calculate according to "energy consumption = power × time", the energy consumption of conveyor belt E = 45 × 0.83 ≈ 37.35 kWh, and the energy consumption of stacker F = 70 × 0.83 ≈ 58.1 kWh. Record the energy consumption values ​​of these two devices. A certain cycle includes 3 operations, and the energy consumption of each device is calculated separately: In operation 1 (unloading), the conveyor belt energy consumption is 30 kWh, and the stacker energy consumption is 40 kWh; in operation 2 (loading), the reclaimer energy consumption is 90 kWh, and the conveyor belt energy consumption is 80 kWh; in operation 3 (transfer), the stacker energy consumption is 50 kWh, and the reclaimer energy consumption is 60 kWh. The total energy consumption of all equipment is added together to obtain the first energy consumption value, which is 30+40+90+80+50+60=350kWh. There are two idle time slots within this cycle: 10:00-10:10 (duration 10 minutes = 10 / 60 ≈ 0.17 hours) and 12:00-12:30 (duration 30 minutes = 30 / 60 = 0.5 hours). The total standby power of the equipment involved in the operation is 15kW (5kW for the stacker + 6kW for the reclaimer + 4kW for the belt conveyor). The energy consumption of each idle time slot is calculated using the formula "standby energy consumption = total standby power × time duration": first idle time slot energy consumption = 15 × 0.17 ≈ 2.55kWh, second idle time slot energy consumption = 15 × 0.5 = 7.5kWh; the total energy consumption is 2.55 + 7.5 = 10.05kWh. Adding the first energy consumption value to the second energy consumption value yields the comprehensive energy consumption index = 350 + 10.05 = 360.05 kWh, which is used to analyze the total energy consumption distribution within the cycle.

[0146] By executing steps 141-144 above, this application achieves accurate measurement of energy consumption in a single operation by calculating the energy consumption of each piece of equipment. This provides foundational data for subsequent summarization of total cycle energy consumption and helps identify high-energy-consuming equipment and processes. By accumulating the energy consumption of all equipment in the cycle, a first energy consumption value reflecting the actual energy consumption of the operation is obtained, providing core data for evaluating the energy efficiency of the operation within the cycle. By calculating the standby energy consumption during idle time, the energy consumption not covered by the first energy consumption value is supplemented, making energy consumption statistics more comprehensive and helping to identify energy-saving potential in idle processes. The comprehensive energy consumption index integrates all energy consumption during operation and standby, comprehensively reflecting the energy consumption situation within the cycle. This provides data support for energy-saving measures such as adjusting the operation sequence and reducing idle time in the next cycle, and helps to systematically reduce energy consumption.

[0147] Figure 3 This application provides a schematic diagram of the structure of a dynamic optimization system for coal loading and unloading energy consumption based on digital twins, as shown in the embodiments of this application. Figure 3The system may include:

[0148] Module 31 is used to create a digital twin model that includes the yard space structure, equipment movement range, and business constraint rules.

[0149] The data acquisition module 32 is used to collect planned data, equipment status and location data, yard resource occupancy data and yard constraint data of bulk coal loading and unloading operations, forming a data set.

[0150] The determination module 33 is used to determine the bulk coal loading and unloading scheme based on the data set and digital twin model. When external conditions change, the scheme regeneration mechanism is triggered. The bulk coal loading and unloading scheme includes the correspondence between the ship's internal holds and the coal type, the correspondence between the ship's holds and the amount of coal, and the loading sequence of the coal type and the ship's holds.

[0151] The calculation module 34 is used to calculate the first energy consumption value of coal loading and unloading for all coal bulk loading and unloading operations in the current cycle after the coal bulk loading and unloading plan is completed, to obtain the comprehensive energy consumption index, and to provide energy-saving guidance information for the next cycle based on the comprehensive energy consumption index.

[0152] This application provides an embodiment of a dynamic optimization system for coal loading and unloading energy consumption based on digital twins to implement the aforementioned dynamic optimization method for coal loading and unloading energy consumption based on digital twins. Therefore, the specific implementation of the dynamic optimization system for coal loading and unloading energy consumption based on digital twins can be found in the embodiment section of the aforementioned dynamic optimization method for coal loading and unloading energy consumption based on digital twins. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0153] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described dynamic optimization method for coal loading and unloading energy consumption based on digital twins.

[0154] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for dynamic optimization of coal loading and unloading energy consumption based on digital twins.

[0155] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0156] The embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the dynamic optimization method for coal loading and unloading energy consumption based on digital twins.

[0157] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0158] The foregoing provides a detailed description of a method and system for dynamic optimization of coal loading and unloading energy consumption based on digital twins, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for dynamic optimization of energy consumption in coal loading and unloading based on digital twins, characterized in that, include: Establish a digital twin model that includes the yard spatial structure, equipment movement range, and business constraint rules; Collect planning data, equipment status and location data, yard resource occupancy data, and yard constraint data for bulk coal loading and unloading operations to form a data set. The yard resource occupancy data includes the status of empty stack spaces. Based on the data set and the digital twin model, a coal bulk cargo loading and unloading scheme is determined. When external conditions change, a scheme regeneration mechanism is triggered. The coal bulk cargo loading and unloading scheme includes the allocation matrix between the ship's internal holds, coal type, and tonnage. After the coal bulk cargo loading and unloading plan is completed, the first energy consumption value of coal loading and unloading for all coal bulk cargo loading and unloading operations in the current cycle is calculated. Combined with the second energy consumption value corresponding to the off-peak time period in the current cycle, a comprehensive energy consumption index is obtained. Based on the comprehensive energy consumption index, energy-saving guidance information is provided for the next cycle. The step of determining a bulk coal loading and unloading scheme based on the dataset and the digital twin model includes: The dataset is dynamically aligned with the digital twin model to eliminate conflicting data in the dataset and generate a valid set of input parameters. Based on the ship berthing window and train arrival time sequence in the set of valid input parameters, and combined with the equipment movement path topology in the digital twin model, the allowable execution time interval for coal bulk cargo loading and unloading tasks in the current period is calculated, and a task sequence mapping table with timestamps is generated. Based on the coal type demand, flow direction attributes and stack tonnage constraints of the ship forecast in the data set, and combined with the preset operation mode, the spatial topological relationship between the stack and the ship's hold is simulated through a digital twin model. With the goal of minimizing the equipment movement distance, an allocation matrix between the ship's hold, coal type, stack position and tonnage is generated within the allowed execution time interval. Based on the allocation matrix between ship hold, coal type, stack position and tonnage, determine the coal type loading sequence and ship hold loading sequence; The task sequence mapping table, the allocation matrix, the coal type loading order, and the ship's hold loading order are input into the digital twin model. Combined with the real-time location status of the equipment, a coal bulk cargo loading and unloading scheme is generated in the form of a loading and unloading sequence Gantt chart.

2. The method according to claim 1, characterized in that, Based on the coal type demand, flow direction attributes, and stack tonnage constraints predicted by ships in the data set, and combined with a preset operation mode, a digital twin model is used to simulate the spatial topology relationship between the stack and the ship's hold. With the goal of minimizing equipment movement distance, an allocation matrix between the ship's hold, coal type, stack location, and tonnage is generated within the allowed execution time interval, including: Based on the aforementioned flow direction attributes, the entire ship's cabins are divided into several independent work groups, which are either foreign trade isolation groups or domestic trade work groups. By combining the preset operation mode, the three-dimensional coordinate data of the stack positions in the yard and the geometric center coordinates of the ship's cabins are called through the digital twin model to establish a distance mapping table from the center point of each stack position to the geometric center point of each cabin of the ship. Based on the coal type requirements and stacking tonnage constraints, for each foreign trade isolation group, corresponding stacking positions are matched according to the coal type requirements. During the matching process, the tonnage allocation satisfies the sum of the ship hold capacities within the group. For the domestic trade operation group, based on the continuity relationship of adjacent ship holds, the same coal type is allocated to a set of ship holds with consecutive positions. Based on the distance mapping table, for a single ship compartment operation group, the spatial straight-line distance between each ship compartment in the group and the stacking position assigned to each ship compartment is accumulated, with the goal of minimizing the accumulated distance value of all ship compartment operation groups, to generate an allocation scheme that meets the coal type requirements and tonnage constraints. The allocation scheme is structured and output to obtain the allocation matrix.

3. The method according to claim 2, characterized in that, The step of outputting the allocation scheme in a structured manner to obtain the allocation matrix includes: Based on the flow direction attributes of the ship's cabin, a unique group identifier is assigned to each independent work group; The structured data is filled in layer by layer according to the group identifier. The first layer of the structured data is the sequence of ship compartment numbers arranged according to the physical compartment order of the ship. The second layer is the coal type name associated with each compartment number. The third layer is the tonnage. The upper limit of the total capacity of the ship compartments in the group and the constraint that empty stacks cannot be loaded with coal are checked simultaneously. Load the stack position coordinate topology into the digital twin model, calculate the spatial straight line space of the stack positions allocated to adjacent cabins within the same work group, and if the spatial straight line distance exceeds the set distance threshold, exchange the stack position allocation relationship of non-adjacent cabins. Based on all spatial straight-line distances and stack allocation relationships, an allocation matrix is ​​generated between ship holds, coal types, stack positions, and tonnage within the allowed execution time interval.

4. The method according to claim 1, characterized in that, The allocation matrix based on the relationship between ship hold, coal type, stack position, and tonnage determines the coal loading sequence and ship hold loading sequence, including: Based on the volatile characteristics of the coal types and the allocation matrix, the coal types are prioritized to obtain the coal loading order. The loading sequence of the ship's hold is determined based on the priority of the group identifier, the loading continuity characteristics within the group, the allocation matrix, and the operation mode.

5. The method according to claim 1, characterized in that, The establishment of a digital twin model that includes the yard spatial structure, equipment movement range, and business constraint rules includes: The stockpile is raster-coded to obtain a reference grid coordinate system; Map the boundary coordinates of the stack positions in the reference grid coordinate system to establish a stack position spatial topology database for characterizing the spatial structure of the stockyard; Based on the equipment's mechanical parameters, the movement boundary is modeled to obtain the equipment's movement range; The rules for foreign trade isolation, the rules for mixed coal risk, the constraints on equipment linkage, and the necessary conditions for unloading are combined into business constraint rules. Based on the aforementioned yard spatial structure, equipment movement range, and business constraint rules, a digital twin model corresponding to the yard is constructed.

6. The method according to claim 1, characterized in that, The calculation of the first energy consumption value of all bulk coal loading and unloading operations within the current period, combined with the second energy consumption value corresponding to the off-peak time periods within the current period, yields a comprehensive energy consumption index, including: For a single bulk coal loading and unloading operation, the single-machine loading and unloading energy consumption of each piece of equipment is calculated based on the operating status of each piece of equipment, including belt conveyors, stacker-reclaimers, monitoring equipment, and ship loaders. The first energy consumption value for coal loading and unloading is obtained by summing up the single-machine loading and unloading energy consumption of all equipment in all bulk coal loading and unloading operations in the current period. The second energy consumption value corresponding to the idle time period in the current cycle is determined based on the standby energy consumption of each device, the duration of the idle time period, and the number of idle time periods. The first energy consumption value and the second energy consumption value are combined to obtain the comprehensive energy consumption index.

7. A dynamic optimization system for coal loading and unloading energy consumption based on digital twins, characterized in that, include: A module is established to create a digital twin model that includes the yard space structure, equipment movement range, and business constraint rules. The data acquisition module is used to collect planned data, equipment status and location data, yard resource occupancy data, and yard constraint data for bulk coal loading and unloading operations, forming a data set. The determination module is used to determine the bulk coal loading and unloading scheme based on the data set and the digital twin model. When external conditions change, the scheme is regenerated. The bulk coal loading and unloading scheme includes the correspondence between the ship's internal holds and the coal type, the correspondence between the ship's holds and the amount of coal, and the loading sequence of the coal type and the ship's holds. The calculation module is used to calculate the first energy consumption value of coal loading and unloading for all coal bulk loading and unloading operations in the current cycle after the coal bulk loading and unloading plan is completed, to obtain the comprehensive energy consumption index, and to provide energy-saving guidance information for the next cycle based on the comprehensive energy consumption index. The step of determining a bulk coal loading and unloading scheme based on the dataset and the digital twin model includes: The dataset is dynamically aligned with the digital twin model to eliminate conflicting data in the dataset and generate a valid set of input parameters. Based on the ship berthing window and train arrival time sequence in the set of valid input parameters, and combined with the equipment movement path topology in the digital twin model, the allowable execution time interval for coal bulk cargo loading and unloading tasks in the current period is calculated, and a task sequence mapping table with timestamps is generated. Based on the coal type demand, flow direction attributes and stack tonnage constraints of the ship forecast in the data set, and combined with the preset operation mode, the spatial topological relationship between the stack and the ship's hold is simulated through a digital twin model. With the goal of minimizing the equipment movement distance, an allocation matrix between the ship's hold, coal type, stack position and tonnage is generated within the allowed execution time interval. Based on the allocation matrix between ship hold, coal type, stack position and tonnage, determine the coal type loading sequence and ship hold loading sequence; The task sequence mapping table, the allocation matrix, the coal type loading order, and the ship's hold loading order are input into the digital twin model. Combined with the real-time location status of the equipment, a coal bulk cargo loading and unloading scheme is generated in the form of a loading and unloading sequence Gantt chart.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the dynamic optimization method for coal loading and unloading energy consumption based on digital twins as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables a dynamic optimization method for coal loading and unloading energy consumption based on digital twins as described in any one of claims 1 to 6.

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