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

Abstract

The invention provides a coal loading and unloading energy consumption dynamic optimization method and system based on digital twinning, and relates to the technical field of coal loading and unloading, and the method comprises the steps: building a digital twinning model comprising a storage yard space structure, an equipment movement range and a business constraint rule; the method comprises the following steps: acquiring plan data, equipment state position data, storage yard resource occupation data and storage yard constraint data of coal bulk cargo loading and unloading operation to form a data set; according to the data set and the digital twinborn model, determining a coal bulk cargo loading and unloading scheme, and when external conditions change, triggering a scheme regeneration mechanism; and after the coal bulk cargo loading and unloading scheme is completed, calculating a first energy consumption value of coal loading and unloading of all coal bulk cargo loading and unloading operations in the current period, obtaining a comprehensive energy consumption index in combination with a second energy consumption value corresponding to the neutral time period in the current period, and providing energy-saving guidance information for the next period based on the comprehensive energy consumption index. Energy-saving and safe operation of coal loading and unloading operation can be achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal handling, and particularly relates to a coal handling energy consumption dynamic optimization method and system based on digital twinning. BACKGROUND

[0002] Coal bulk handling is a key link in the coal logistics supply chain, mainly involving business scenarios such as train unloading, yard stacking, and ship loading. In this process, a series of business rules must be followed, such as the need for coal type matching for stacking and handling, the incompatibility of stacker and reclaimer operations at the same pile position, the existence of a maximum coal loading limit for stacking (such as 27,000 tons), and the inadmissibility of loading coal into empty piles. At the same time, energy saving (such as preferential selection of the smallest stack, shortening of equipment movement distance) and safety (such as collision prevention and stopping of unloading under high temperature conditions) requirements must be considered. Therefore, how to achieve energy saving and safe operation of the coal handling process while meeting multiple constraints is an important problem that needs to be solved in the industry.

[0003] In the prior art, the formulation of a coal bulk handling scheme relies on manual experience or traditional scheduling systems. Manual scheduling requires the combination of train arrival time, ship berthing dynamics, coal type demand, and equipment status information to manually allocate pile positions, plan equipment operation paths and sequences; although traditional scheduling systems can achieve automatic verification of some rules, they lack the ability to model the overall structure of the yard space, the movement range of the equipment, and complex business constraints. In actual operation, the scheme is often based on static data, and when external conditions (such as train forecast changes, ship dynamic adjustments, and equipment sudden failures) change, it is difficult to quickly respond and generate an optimal adjustment scheme, and only local corrections can be made through manual intervention.

[0004] The main drawback of the prior art is the inability to dynamically optimize and globally control energy consumption during the coal handling process. On the one hand, due to the lack of integrated modeling of yard resources, equipment status, and business constraints, equipment occupation conflicts can easily occur during scheme formulation; on the other hand, energy consumption calculations are often limited to the energy consumption of a single operation of the equipment, without considering the standby energy consumption of the equipment during the operation gap period, and it is difficult to systematically reduce energy consumption by optimizing equipment movement distance and operation timing, which cannot meet the industry's demand for energy saving and consumption reduction. In addition, the lag and limitations of scheme adjustment in the face of changes in external conditions further exacerbate the contradiction between efficiency and energy consumption, and a technical solution that can dynamically adapt to changes in the scene and globally optimize energy consumption is urgently needed. SUMMARY

[0005] The present application aims to provide a coal handling energy consumption dynamic optimization method and system based on digital twinning to solve the problem of the inability to meet the industry's demand for energy saving and consumption reduction in the prior art.

[0006] To solve the above technical problems, in a first aspect, the application provides a coal loading and unloading energy consumption dynamic optimization method based on digital twinning, comprising: establishing a digital twinning model containing a yard space structure, a device movement range, and business constraint rules; collecting plan data, device state position data, yard resource occupation data, and yard constraint data of coal bulk cargo loading and unloading operations to form a data set, wherein the yard resource occupation data includes empty racking position states; determining a coal bulk cargo loading and unloading scheme according to the data set and the digital twinning model, triggering a scheme regeneration mechanism when external conditions change, wherein the coal bulk cargo loading and unloading scheme includes an allocation matrix between ship cabins, coal types, and tonnages; after the completion of the coal bulk cargo loading and unloading scheme, calculating a first energy consumption value of all coal bulk cargo loading and unloading operations in the current period, combining a second energy consumption value corresponding to an empty time period in the current period to obtain a comprehensive energy consumption index, and providing energy-saving guidance information for the next period based on the comprehensive energy consumption index.

[0007] Optionally, the determination of the coal bulk cargo loading and unloading scheme according to the data set and the digital twinning model comprises: dynamically aligning the data set with the digital twinning model to eliminate conflicting data in the data set and generate a set of valid input parameters; calculating an allowed execution time interval of the coal bulk cargo loading and unloading task in the current period according to a ship berthing window period and a train arrival time sequence in the set of valid input parameters, combining a device movement path topological relationship in the digital twinning model, and generating a task sequence mapping table with a timestamp; based on coal type demand, flow direction attributes, and racking tonnage constraints of a ship forecast in the data set, combining a preset operation mode, simulating a space topological relationship between racking and cabins through the digital twinning model, and generating an allocation matrix between cabins, coal types, racking, and tonnages in the allowed execution time interval with the goal of minimizing device movement distance; determining a coal type loading sequence and a cabin loading sequence based on the allocation matrix between cabins, coal types, racking, and tonnages; inputting the task sequence mapping table, the allocation matrix, the coal type loading sequence, and the cabin loading sequence into the digital twinning model, combining a real-time position state of a device, and generating a coal bulk cargo loading and unloading scheme in the form of a loading and unloading time sequence Gantt chart.

[0008] Optionally, based on the predicted coal demand, flow direction attribute and pile position tonnage constraint in the data set, combined with the preset operation mode, the spatial topological relationship between the pile position and the ship cabin is simulated through the digital twin model, and an allocation matrix between the ship cabin, coal type, pile position and tonnage is generated within the allowed execution time interval to minimize the equipment moving distance, including: According to the flow direction attribute, all ship cabins of the ship are divided into several independent operation groups, and the independent operation group is an export isolation group or an import operation group; Combined with the preset operation mode, the three-dimensional coordinate data of the pile position in the yard and the geometric center coordinates of the ship cabin are called through the digital twin model, and a distance mapping table from the center point of each pile position to the geometric center point of each ship cabin of the ship is established; According to the coal demand and pile position tonnage constraint, for each export isolation group, the corresponding pile position is matched according to the coal demand, and in the matching process, the tonnage allocation satisfies the sum of the cabin capacity in the group; for the import operation group, according to the continuity relationship of adjacent cabins, the same coal type is allocated to the cabin set with continuous position; Based on the distance mapping table, for a single ship cabin operation group, the spatial straight line distance between each cabin in the group and the pile position allocated to each cabin is accumulated, and an allocation scheme satisfying the coal demand and tonnage constraint is generated to minimize the accumulated distance value of all cabin operation groups; The allocation scheme is structured and output to obtain an allocation matrix.

[0009] Optionally, the allocation scheme is structured and output to obtain an allocation matrix, including: Based on the flow direction attribute of the cabin, a unique group identifier is assigned to the independent operation group; According to the group identifier, the structured data is filled layer by layer, the first layer of the structured data is the cabin number sequence arranged according to the physical cabin order of the ship, the second layer is the coal type name associated with each cabin number, and the third layer is the tonnage, which synchronously verifies the constraints of the upper limit of the total capacity of the cabins in the group and the empty pile position cannot load coal; Load the pile position coordinate topology in the digital twin model, calculate the spatial straight line distance of the pile positions allocated by adjacent cabins in the same operation group, and if the spatial straight line distance exceeds the set distance threshold, exchange the pile position allocation relationship of non-adjacent cabins; Based on all spatial straight line distances and pile position allocation relationships, an allocation matrix between the ship cabin, coal type, pile position and tonnage is generated within the allowed execution time interval.

[0010] Optionally, the allocation matrix between the ship cabin, coal type, pile position and tonnage is used to determine the coal loading sequence and the cabin loading sequence, including: According to the volatile characteristic value of the coal type and the allocation matrix, the coal type is prioritized and sorted to obtain the coal loading sequence; The order of loading the hold is determined according to the priority of the group identifier, the loading continuity characteristics within the group, the allocation matrix and the operation mode.

[0011] Optionally, establishing a digital twin model including the yard spatial structure, equipment movement range, and business constraint rules includes: Carry out raster coding processing on the storage yard to obtain the reference grid coordinate system; Map the boundary coordinates of the stack positions in the reference grid coordinate system and establish a stack position spatial topology relationship library to characterize the spatial structure of the yard; Based on the mechanical parameters of the equipment, the moving boundary is modeled to obtain the equipment movement range; Combine foreign trade isolation rules, mixed coal risk rules, equipment linkage constraints, and necessary unloading conditions into business constraint rules; Based on the yard's spatial structure, equipment movement range, and business constraint rules, a digital twin model corresponding to the yard is constructed.

[0012] Optionally, the first energy consumption value of coal loading and unloading of all bulk coal loading and unloading operations in the current cycle is calculated, and combined with the second energy consumption value corresponding to the idle time period in the current cycle to obtain a comprehensive energy consumption index, including: For a single bulk coal loading and unloading operation, calculate the loading and unloading energy consumption of each piece of equipment based on its operating status. The equipment includes conveyors, stackers and reclaimers, monitoring equipment, and ship loaders. Accumulate the single-machine loading and unloading energy consumption of all equipment in all coal bulk loading and unloading operations in the current cycle to obtain the first energy consumption value of coal loading and unloading; Determine a second energy consumption value corresponding to the idle time period in the current cycle according to the standby energy consumption of each device, the length of the idle time period, and the number of the idle time periods; The first energy consumption value and the second energy consumption value are combined to obtain a comprehensive energy consumption index.

[0013] Secondly, this application provides a digital twin-based dynamic optimization system for coal loading and unloading energy consumption, including: Establish a module for building a digital twin model that includes the yard's spatial structure, equipment movement range, and business constraint rules; The collection module is used to collect the planning data of bulk coal loading and unloading operations, equipment status and location data, yard resource occupancy data, and yard constraint data to form a data set; a determination module, configured to determine a bulk coal loading and unloading plan based on the data set and the digital twin model, and trigger a regeneration mechanism for the plan when external conditions change, wherein the bulk coal loading and unloading plan includes a correspondence between the ship's holds and the types of coal, a correspondence between the holds and the amount of coal, and a loading sequence of the coal types and the holds; a calculation module, configured to calculate a first energy consumption value of coal loading and unloading of all coal loading and unloading operations in a current period after completion of the coal bulk cargo loading and unloading scheme, obtain a comprehensive energy consumption index, and provide energy-saving guidance information for a next period based on the comprehensive energy consumption index.

[0014] In a third aspect, the present application provides an electronic device, comprising: a memory, configured to store a computer program; a processor, configured to implement the steps of the coal loading and unloading energy consumption dynamic optimization method based on digital twinning when the computer program is executed.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program can implement the steps of the coal loading and unloading energy consumption dynamic optimization method based on digital twinning when the computer program is executed by a processor.

[0016] The coal loading and unloading energy consumption dynamic optimization method based on digital twinning provided by the present application comprises the following steps: establishing a digital twinning model comprising a yard space structure, a device moving range and a business constraint rule; collecting plan data, device state position data, yard resource occupation data and yard constraint data of coal bulk cargo loading and unloading operations to form a data set, wherein the yard resource occupation data comprises an empty stack position state; determining a coal bulk cargo loading and unloading scheme according to the data set and the digital twinning model, triggering a scheme regeneration mechanism when external conditions change, wherein the coal bulk cargo loading and unloading scheme comprises an allocation matrix between ship cabins, coal types and tonnages; calculating a first energy consumption value of coal loading and unloading of all coal bulk cargo loading and unloading operations in a current period after completion of the coal bulk cargo loading and unloading scheme, obtaining a comprehensive energy consumption index in combination with a second energy consumption value corresponding to an empty time period in the current period, and providing energy-saving guidance information for a next period based on the comprehensive energy consumption index.

[0017] The above-mentioned method provided by the present application has the following advantages: (1) By including unloading necessary conditions (such as coal type matching for stacking, stacker and reclaimer cannot operate at the same stack position at the same time, maximum coal loading capacity of the stack is 27000 tons, empty stack position cannot load coal, corresponding stack position cannot be used when the device fails, etc.) in the digital twinning model, it is ensured that the generated loading and unloading scheme strictly follows the business rules, avoids illegal operations from the source, and guarantees the standardization and stability of the operation process. The digital twinning model can include safety-related constraints (such as anti-collision requirements for a stack position interval between a stacker and a reclaimer, no unloading at a temperature above 40 degrees Celsius, etc.), which ensures that the scheme meets the safety specifications through simulation verification, reduces the risk of device collision and high-temperature operation, and provides technical support for operation safety.

[0018] (2) Generate a time-stamped task sequence based on planned data (such as train arrival time), and trigger the plan regeneration mechanism in combination with changes in external conditions. This can achieve the goal of "ensuring that all trains operate simultaneously as much as possible, reducing equipment occupancy conflicts and waiting time". At the same time, optimize the plan with "minimum total operation time" as the core indicator, significantly improving the overall efficiency of unloading and subsequent operations.

[0019] (3) On the one hand, the digital twin model is used to simulate the equipment movement path, with "the stacker movement distance is as small as possible" and "minimum stacking priority" as the optimization direction, which directly reduces the equipment operation energy consumption; 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", the 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 the loading and unloading process.

[0020] (4) It supports the selection of operation modes (stocking mode, balancing mode, high-production mode) based on the actual situation of the yard, and flexibly responds to the priority requirements under different modes through the digital twin model (for example, the stocking mode gives priority to stacking positions with small yard capacity, and the high-production mode gives priority to shortening the unloading time), thereby improving the adaptability of the solution in complex scenarios.

[0021] (5) The design of the distribution matrix of the ship's cabin, coal type and tonnage can directly adapt to the energy-saving and efficiency requirements of the loading process, such as realizing "the shorter the moving distance of the reclaimer, the better" and "giving priority to stacking positions with small stacking positions to shorten the belt distance". In addition, the loading mode can be recommended in combination with the ship's cabin information to promote the coordinated optimization of the unloading and loading links and improve the operating efficiency of the overall logistics chain.

[0022] (6) By dynamically processing data, combining the timing of ships and trains with the topology of equipment paths to generate task sequences, simulating spatial topology and generating an allocation matrix with the goal of minimizing the equipment movement distance, it can meet the necessary conditions such as coal type matching and tonnage constraints in the unloading business, meet the requirements of energy saving (shortening the equipment movement distance) and scheduling production according to arrival time, and improve loading and unloading efficiency and energy saving effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0024] Figure 1 A flowchart of a method for dynamic optimization of energy consumption in coal loading and unloading based on digital twins provided in an embodiment of the present application; Figure 2 An application scenario schematic diagram of a coal loading and unloading energy consumption dynamic optimization method based on digital twinning provided by an embodiment of the present application is provided. Figure 3 A structure schematic diagram of a coal loading and unloading energy consumption dynamic optimization system based on digital twinning provided by an embodiment of the present application is provided. DETAILED DESCRIPTION

[0025] In order to solve the problem that the prior art cannot meet the fine energy saving and consumption reduction requirements of the industry, an embodiment of the present application provides a coal loading and unloading energy consumption dynamic optimization method based on digital twinning. The method adopts the following concept: by constructing a digital twinning model containing a yard structure, a device range and a business rule, integrating operation plans, device states, yard resources and other data, generating a loading and unloading scheme containing a ship cabin, a coal type and a tonnage distribution, and automatically triggering scheme updating when external conditions change; at the same time, by calculating the comprehensive indicators of operation energy consumption and idle energy consumption, energy saving guidance is provided for the subsequent period. This scheme realizes global visualization and dynamic optimization with the help of digital twinning, solves the problems of device conflicts and adjustment lag in traditional methods, and realizes systematic energy saving through whole-cycle energy consumption control, effectively balancing operation efficiency and energy consumption cost.

[0026] In order to enable personnel in the technical field to better understand the present application scheme, the present application will be further described in detail below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0027] The core of the present application is to provide a coal loading and unloading energy consumption dynamic optimization method based on digital twinning. The flowchart of a specific embodiment of the method is shown in FIG. 1, which includes the following steps. Figure 1 S11, a digital twinning model containing a yard space structure, a device movement range and a business constraint rule is established.

[0028] ​In S11, the yard spatial structure can refer to the physical layout information of the yard, including the distribution, boundaries and relative positional relationships of the stacking positions; the equipment movement range refers to the movement boundaries of movable equipment such as stackers and reclaimers, such as the track range and the movement radius of the robotic arm; 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 trade and domestic trade goods), mixed coal risk rules (i.e. different types of coal 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 empty stacks cannot be loaded with coal; the digital twin model is a virtual replica of the actual yard and operating environment, which can map the physical entity status in real time and support simulation optimization.

[0029] The digital twin model corresponds to the storage yard. Figure 2 As shown, in an exemplary stockpile yard, four car dumpers, CD1, CD2, CD3, and CD4, may be installed, commencing operation upon arrival of a train. Four conveyor belts (BF1, BF2, BF3, and BF4) are also installed beneath the car dumper hoppers, transporting coal. Each belt conveyor is equipped with a belt scale corresponding to the car dumper, coded BSBF1, BSBF2, BSBF3, and BSBF4, respectively. These scales are used to obtain electronic scale information related to the corresponding car dumper, including instantaneous values, accumulated values, and the number of dumping sections. For safe transportation, this embodiment of the application features belt conveyor transfer towers at various belt positions: T4, T5, T6, T7, T8, T9, T10, and T11. BH1-3, BH1-4, BH2-3, BH2-4, BH3-3, BH3-4, BH4-3, and BH4-4 represent the codes for the intermediate belts of the corresponding longitudinal turning lines. There are three intermediate belts on the loading and unloading line: BJ1-1, BJ2-1, and BJ3-1. MSBQ1, MSBQ2, and MSBQ3 represent the corresponding electromagnetic separators, respectively. There are four stacker belts: BD1, BD2, BD3, and BD4. There are four reclaimer belts: BQ1, BQ2, BQ3, and BQ4. There are three ship loaders: SL1, SL2, and SL3. There are three conveyor belts connecting the ship loaders: BM1, BM2, and BM3. The present application has four stackers in the stockpile yard, namely S1, S2, S3, and S4. The present application has multiple reclaimers in the stockpile yard: R1-1, R1-2, R2-1, R2-2, R3-1, and R3-2. For example, the stockpile yard can have 6 rows and 4 columns, so the total number of stacks in the stockpile yard can be 24, such as stack 101, stack 102, stack 103, ..., stack 604, etc., and there are multiple types of coal, such as coal type A, coal type B, coal type C, coal type D, and coal type E.

[0030] In the embodiments of the present application, the establishment of the digital twin model can be achieved through the following steps 111 to step 115, which will not be described here.

[0031] S12, collecting plan data, equipment state position data, stockyard resource occupation data and stockyard constraint data of coal bulk cargo handling operation to form a data set, the stockyard resource occupation data including empty stack position state.

[0032] In S12, the plan data includes train arrival plan (train number, coal type, estimated arrival time), ship berthing plan (ship name, voyage, coal type demand, berthing / departure time) and the like; the equipment state position data can refer to the running state (operation / idling / fault) and real-time position (for example, grid coordinates) of the equipment such as stacker and reclaimer; the stockyard resource occupation data includes the occupation state (occupied / empty stack position) of each stack position, the stored coal type and tonnage; the stockyard constraint data refers to fixed conditions for limiting operation, such as maximum stack capacity of 27000 tons, unloading at high temperature above 40℃ and the like; the data set is a unified data set integrating the above various types of data, providing basic information for scheme development.

[0033] In the embodiments of the present application, the train arrival plan is collected through the information management system of the terminal, equipment sensors and manual input, such as the coal type E of train D, the estimated arrival at 8:00, the ship berthing plan, such as the voyage 1 of ship F, the demand for coal type E of 5000 tons, the berthing at 9:00 and the like; the equipment state position data of the stacker M is collected through the positioning device and state monitor on the equipment, such as the position of grid coordinates (20, 30) and the state (idling) of the reclaimer N, the position (40, 50) and the state (operation) and the like; the stockyard resource occupation data is collected through the stockyard monitoring system, such as the stack position A (occupied, coal type E, 20000 tons) and the stack position B (empty stack position); at the same time, the stockyard constraint data such as the maximum stack capacity of 27000 tons and unloading at high temperature is inputted; finally, these data are integrated to form a complete data set.

[0034] S13, determining the coal bulk cargo handling scheme according to the data set and the digital twin model, triggering the scheme regeneration mechanism when the external conditions change, the coal bulk cargo handling scheme including the allocation matrix between the ship cabin, the coal type and the tonnage.

[0035] In S13, the change of the external condition can refer to a change of train prediction information, a change of ship prediction information, a change of berthing time and unberthing time of the ship, a change of train arrival time, a change of ship assembly information, etc., wherein the train prediction information includes train number, coal type and tonnage, the ship prediction information includes ship name, voyage, coal type, tonnage, flow direction and ship cabin number, and the ship assembly information can refer to how to assemble, which ship cabin to assemble which coal type, tonnage and loading sequence. The distribution matrix is structured data recording the correspondence between the ship cabin, the coal type, the stack position and the tonnage.

[0036] In the embodiment of the present application, the generation of the coal bulk cargo loading and unloading scheme can be realized by the following steps 131-135, which will not be repeated here.

[0037] S14, after the completion of the coal bulk cargo loading and unloading scheme, a first energy consumption value of the coal loading and unloading of all coal bulk cargo loading and unloading operations in the current period is calculated, a second energy consumption value corresponding to the idle 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.

[0038] In S14, the first energy consumption value is the total running energy consumption of the equipment (belt, stacker-reclaimer, etc.) in all operations in the current period; the second energy consumption value is the total standby energy consumption of the equipment in the idle time period (idle time without operation) in the current period; and the comprehensive energy consumption index is the sum of the first energy consumption value and the second energy consumption value, which is used to reflect the total energy consumption level in the period. The energy-saving guidance information includes: the amount of operation, the process (device string) used for operation, the flow rate used for operation, and finally the most power saving. It should be noted that, since the comprehensive energy consumption index of the current period can provide energy-saving guidance information for the next period, the comprehensive energy consumption index of the last period can also provide energy-saving guidance information for the current period.

[0039] In the embodiment of the present application, the generation of the comprehensive energy consumption index can be realized by the following steps 141-144, which will not be repeated here.

[0040] In one specific example, a certain coal terminal plans to establish a digital twin model. First, the stockyard is divided into 10m x 10m grids, and each grid coordinate is represented by (X, Y), for example, (10, 20) represents the 10th column and 20th row grid. Then, mark the pile position A (boundary coordinates X1-X3, Y1-Y3), pile position B (boundary coordinates X4-X6, Y4-Y6), etc. in the coordinate system, and record that pile position A is adjacent to pile position B. Then, according to the track length of the stacker M, which is 500m, the moving range of the model is X0-X500, Y fixed value. At the same time, the rules such as "coal type C can only be stacked in the marked C pile position" and "stacker and reclaimer cannot enter the same pile position area at the same time" are included. Finally, a complete digital twin model is formed, which can reflect the stockyard layout, equipment position and rule constraints in real time. Before operation, a certain coal terminal collects data: plan data shows that train G (coal type H, 3000 tons, expected to arrive at 10:00), ship I (voyage 2, needs coal type H 4000 tons, 11:00 berthing); equipment status position data shows that stacker J is idle at (15, 25) and reclaimer K is working at (35, 45); stockyard resource occupation data shows that pile position C (occupied, coal type H, 25000 tons), pile position D (empty pile position); stockyard constraint data includes pile height limit 27000 tons, and unloading above 40℃. After integrating these data, a data set is formed for subsequent scheme development. Based on the model of S11 and the data set of S12, the conflicting data of "pile position D assigned to two trains" is found and removed to generate valid parameters; according to the berthing of ship I from 11:00 to 15:00 and the arrival of train G at 10:00, a task sequence is generated: 10:00-10:30 train G unloading to pile position D, 11:00-14:00 loading ship I from pile position D. Since ship I is for domestic trade, divide its three ship holds into one domestic group, call the coordinates of pile position D and ship holds 1-3, and calculate the distances of 100m, 120m and 110m respectively; match pile position D with coal type H, assign ship hold 1 to load 1500 tons, ship hold 2 to load 1500 tons, and ship hold 3 to load 1000 tons (total 4000 tons, not exceeding pile height limit), and optimize the sequence of ship hold 1→3→2 (total distance is shorter); finally, a Gantt chart is generated to show the time arrangement of each task. If train G is late, the system will automatically recalculate the time interval and update the scheme. In a certain period, the unloading of train G and the loading of ship I are completed: the belt runs for 0.5 hours (power 50kW) with energy consumption of 25kWh, and the stacker J runs for 0.5 hours (power 60kW) with energy consumption of 30kWh; the reclaimer K runs for 3 hours (power 40kW) with energy consumption of 120kWh, and the belt runs for 3 hours (power 50kW) with energy consumption of 150kWh, and the first energy consumption value is 25+30+120+150=325kWh.The idle time period in the cycle is from unloading to loading (0.5 hours), the stacker J is in standby (power 5kW) and the energy consumption is 2.5kWh, the reclaimer K is in standby (power 6kW) and the energy consumption is 3kWh, and the second energy consumption value is 5.5kWh. The comprehensive energy consumption index is 325+5.5=330.5kWh. It is found that the idle time can be compressed, which guides the optimization of the next cycle to reduce the standby energy consumption.

[0041] The coal loading and unloading energy consumption dynamic optimization method based on digital twinning provided in the application realizes the digital integration of the yard space, equipment movement and business rules by constructing a digital twinning model, provides a virtual simulation environment for the development of subsequent operation schemes, can simulate the operation process in advance to avoid conflicts, and guarantees the compliance and safety of the operation; by collecting and integrating various data, comprehensive and accurate basic information is provided for the development of operation schemes, ensuring that the scheme can be in line with the actual operation conditions and avoiding unreasonable schemes due to incomplete information; by dynamically processing data, simulating space relationships and optimizing operation sequences, an efficient loading and unloading scheme that meets the constraint conditions is generated, which can respond to changes in external conditions and adjust in real time, reducing equipment conflicts and waiting time, improving operation efficiency and flexibility; by calculating the comprehensive index of operation energy consumption and idle standby energy consumption, comprehensive control of energy consumption is realized, providing data basis for energy saving optimization in the next cycle, which is helpful for systematic reduction of energy consumption and improvement of operation economy.

[0042] In some optional embodiments, S11, the digital twinning model containing the yard space structure, the equipment movement range and the business constraint rules is established, including: Step 111, performing grid encoding processing on the yard to obtain a reference grid coordinate system.

[0043] In step 111, the grid encoding processing is a process of dividing the entire yard area into a plurality of regular grids according to a fixed size, and giving each grid a unique identification (such as a coordinate value); the reference grid coordinate system is a coordinate system formed by grid encoding, which is used for accurately positioning any position in the yard, and the coordinates of each grid can uniquely correspond to the physical area in the real yard, providing a basis for the digitization of subsequent space information.

[0044] In the embodiments of the present application, first, the size of the grid is determined (such as selecting a 5m*5m or 10m*10m grid according to the actual size of the yard and the operation accuracy requirement); then, a two-dimensional coordinate system is established with a certain fixed point (such as the northwest corner vertex) of the yard as the origin, the horizontal direction as the X axis and the vertical direction as the Y axis; next, each grid is assigned a unique coordinate (such as (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 yard is formed to realize the digital segmentation of the yard space. For example, a certain yard is 500m long and 300m wide, a 10m*10m grid size is selected, and the northwest corner is taken as the origin. A total of 50 columns (X=1 to 50) and 30 rows (Y=1 to 30) of grids can be divided, and the grid (10, 15) corresponds to the area of 100-110m in the X direction and 150-160m in the Y direction in reality.

[0045] Step 112, mapping the boundary coordinates of the stacks in the reference grid coordinate system to establish a stack space topology relationship library for representing the spatial structure of the yard.

[0046] In step 112, the boundary coordinates of the stacks refer to the grid range occupied by each stack in the reference grid coordinate system, which is usually represented by the minimum and maximum grid coordinates (such as the boundary of stack A is X1-X3, Y1-Y3); the stack space topology relationship library is a database recording the spatial relationships (such as adjacent, containing, and separated) between all stacks, which is used to intuitively reflect the layout structure of the stacks in the yard.

[0047] In the embodiments of the present application, in the reference grid coordinate system obtained in step 111, the grid range corresponding to the physical boundary of each stack is determined, and the minimum X coordinate, the maximum X coordinate, the minimum Y coordinate, and the maximum Y coordinate (i.e. the boundary coordinates) of each stack are recorded; then, by comparing the boundary coordinates of the stacks, the spatial relationships between them are determined - if there are adjacent grids between the boundary coordinates of two stacks (such as the maximum X coordinate of stack A plus 1 equals the minimum X coordinate of stack B), it is determined that they are adjacent; if the boundary coordinates of stack C are completely contained in the boundary coordinates of stack D, it is determined that they are in a containing relationship; finally, the boundary coordinates of all stacks and the topology relationships between them are sorted into the library to form the stack space topology relationship library. For example, in the grid system of step 111, the boundary coordinates of stack M are X5-X8, Y10-Y13, and the boundary coordinates of stack N are X9-X12, Y10-Y13. By comparison, it is found that they are adjacent in the X direction, and therefore the topology relationship library records that "stack M is adjacent to stack N".

[0048] Step 113, modeling the moving boundary based on the mechanical parameters of the equipment to obtain the moving range of the equipment.

[0049] In step 113, the equipment mechanical parameters refer to the physical performance parameters of the mobile equipment such as the stocker, the reclaimer, etc., including the track length, the maximum activity radius of the mechanical arm, the movement speed limit, the turning angle limit, etc.; the mobile boundary modeling is a process of delimiting the spatial boundary of the equipment activity in the reference grid coordinate system according to the equipment mechanical parameters; the equipment movement range refers to the set of all grid areas that can be reached by the equipment in the operation, used to limit the activity area of the equipment to avoid exceeding the physical range, including the constraint that the stocker and the reclaimer cannot simultaneously operate in one stack position.

[0050] In the embodiments of the present application, first, the mechanical parameters of the equipment such as the stocker, the reclaimer, etc. are collected, such as the track laying range of the stocker (X direction from Xa to Xb, Y direction fixed as Yc), the maximum activity radius R of the mechanical arm of the reclaimer; then, in the reference grid coordinate system obtained in step 111, the movement boundary of the equipment is delimited according to these parameters - for the track-type equipment, the movement range is the grid area covered by the track (such as Xa-Xb, Yc); for the rotary equipment, a circular or sector activity area is delimited in the grid system with the fixed base coordinates (X0, Y0) of the equipment as the center and the activity radius R as the radius; finally, the delimited boundary is converted into specific grid coordinate range to obtain the equipment movement range. For example, the track of the stocker B is laid at the position of X5-X50, Y10, and according to the track length parameter, its movement range in the reference grid is all the grids corresponding to X5-X50, Y10.

[0051] In step 114, the foreign trade isolation rule, the mixed coal risk rule, the equipment linkage constraint and the unloading necessary condition are combined into the business constraint rule.

[0052] In step 114, the foreign trade isolation rule refers to the rule that the foreign trade coal and the domestic trade coal must be stored or operated in physical space isolation to prevent cross contamination; the mixed coal risk rule refers to the rule that different coal types (such as power coal, coking coal) cannot be mixed for storage or handling to avoid affecting the coal quality; the equipment linkage constraint refers to the logical limit that needs to be followed when multiple equipment operate cooperatively (such as the stocker and the reclaimer cannot simultaneously enter the same stack position area to reduce the equipment occupation conflict, the anti-collision constraint of stacking and reclaiming interval one stack position, etc.); the unloading necessary condition refers to the premise that must be met for unloading operation (such as the coal type and the stack position label coal type match, the stacking does not reach the maximum capacity of 27000 tons, the non-empty stack position can load coal, the temperature above 40 degrees Celsius does not unload, etc.); the business constraint rule is the set of the above four types of rules, used to regulate the operation process and guarantee the compliance and safety.

[0053] In the embodiments of the present application, first, the specific requirements of the foreign trade isolation rules are sorted out, such as "at least 2 grids are required to be separated between the foreign trade coal stacking position and the domestic trade coal stacking position"; then, the details of the mixed coal risk rules are clarified, such as "the area marked as coal type D cannot be stacked with coal type E"; then, the content of the equipment linkage constraint is determined, such as "the stacker and the reclaimer cannot enter the grid area of X10-X20, Y5-Y15 at the same time"; then, the unloading necessary conditions are sorted out, such as "the unloading coal type must be consistent with the marked coal type of the target stacking position", "the current stacking tonnage of the stacking position + the tonnage to be unloaded ≤ 27000 tons", "the empty stacking position cannot be used for unloading"; finally, these rules are classified and integrated to form a complete set of business constraint rules, which are used as the compliance judgment standard for subsequent scheme development. For example, in the integrated rules, there are not only the isolation requirements such as "foreign trade coal needs to be stored in the X1-X20 area, and domestic trade coal needs to be stored in the X30-X50 area", but also the mixed coal restrictions such as "the same stacking position cannot store coal type F and coal type G".

[0054] In step 115, based on the yard space structure, the equipment movement range and the business constraint rules, a digital twin model corresponding to the yard is constructed.

[0055] In step 115, the yard space structure refers to the yard layout information reflected by the stacking position space topological relationship library established in step 112; the equipment movement range is the movable grid area of each device obtained in step 113; the business constraint rules are various operation rules integrated in step 114; the digital twin model is a digital model corresponding to the real yard, which is formed by integrating the above three types of information into a virtual environment, and can real-time map the state of the real yard (such as stacking position occupation, equipment position) and support operation simulation.

[0056] In the embodiments of the present application, first, the stacking position space topological relationship library obtained in step 112 is imported into a virtual modeling platform, and the space layout of the yard is restored in the platform, including the positions, boundaries and mutual relationships of each stacking position; then, the equipment movement range obtained in step 113 is loaded into the model, and the movable area of each device is marked in the virtual yard; then, the business constraint rules integrated in step 114 are converted into logical codes (such as condition judgment statements) recognizable by the model, and embedded into the virtual model; finally, the real-time data synchronization between the virtual model and the real yard is realized through a data interface (such as real-time acquisition of equipment position, stacking position occupation state), so that the virtual model can dynamically reflect the real state, and the construction of the digital twin model is completed. For example, in the virtual platform, the positions and adjacent relationships of stacking positions A1, A2 and A3 in step 112 are first restored, then the movement ranges of the stacker B and the reclaimer C in step 113 are marked, and the rules such as foreign trade isolation in step 114 are embedded, and finally the digital twin model of terminal A which can be real-time updated is formed.

[0057] In one specific example, the stockyard area of coal terminal A is 600m x 400m, and to realize spatial digitization, a grid size of 8m x 8m is used for raster coding. Taking the southwest corner of the stockyard as the origin, the horizontal (east-west direction) as the X axis, and the vertical (north-south direction) as the Y axis, a total of 75 columns (X = 1 to 75) and 50 rows (Y = 1 to 50) of grids are divided, and the coordinates of each grid are represented by (X, Y). Among them, grid (20, 30) corresponds to the area of 152-160m in the X direction and 232-240m in the Y direction in reality, and through this coordinate, the equipment or pile position in this area can be accurately located. Based on the grid system of coal terminal A in step 111, the boundary coordinates of the 30 piles in the field are mapped: the boundary of pile A1 is X10-X15, Y20-Y25, the boundary of pile A2 is X16-X21, Y20-Y25, and the boundary of pile 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. These information (including the boundary coordinates of each pile and the relationship with each other) are recorded into the system to form the pile space topology relationship library of terminal A. Coal terminal A has two main mobile devices: stacker B and reclaimer C. Stacker B is track-mounted, with the track laid along the X direction, ranging from X8-X60, Y15 (fixed). According to its mechanical parameters, after mobile boundary modeling, its moving range is all the grids corresponding to X8-X60, Y15 in the reference grid; reclaimer C is rotary, with the base located at grid (30, 25), and the maximum activity radius of the mechanical arm is 20m (corresponding to 2.5 8m grids), so its moving range is a circular area with (30, 25) as the center and a radius of 2.5 grids, covering grids (28, 23) to (32, 27). These ranges are recorded into the system as spatial restrictions for device operation. The business constraint rules of coal terminal A are integrated as follows: the foreign trade isolation rule stipulates that "foreign trade coal can only be stacked in the grid area of X1-X25, Y1-Y50, and domestic trade coal can only be stacked in the area of X30-X75, 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 piles"; the device linkage constraint stipulates that "the operation area of stacker B and reclaimer C cannot overlap"; and the unloading necessary conditions include "the unloading coal type must be consistent with the pile marking coal type", "the current tonnage of the pile + the unloading tonnage ≤27000 tons", and "empty piles (not marked with coal type) cannot be unloaded". These rules collectively constitute the business constraint rule system of terminal A.The coal terminal A builds a digital twin model in the virtual modeling platform: first, the pile space topological relation library of step 112 is imported, and the positions and adjacent relations of 30 piles in the virtual environment are restored; then the moving ranges of the stacker B (X8-X60, Y15) and the reclaimer C (from (28, 23) to (32, 27)) in step 113 are loaded, which are marked in different colors in the virtual yard; then the business constraint rules in step 114 are converted into logic codes (for example, when the system detects that the foreign trade coal tries to be stored in the domestic trade area, an illegal reminder is automatically triggered); finally, the coal type, tonnage and equipment position data of the pile in the reality are obtained in real time through the sensor interface and are synchronized to the virtual model. The finally formed digital twin model can intuitively display the yard state and can simulate the operation process.

[0058] By performing the above steps 111-115, the reference grid coordinate system formed by the grid encoding processing of the present embodiment converts the physical yard into a calculable digital space, which provides a unified spatial reference standard for subsequent pile positioning, equipment moving path planning, etc., and ensures the accuracy and consistency of the spatial information; by establishing the pile space topological relation library, the distribution and mutual position relation of the piles in the yard are clearly presented, which provides an intuitive spatial reference for subsequent equipment path planning, pile allocation, etc., and helps to avoid low efficiency or safety problems caused by space conflicts in the operation of the equipment; by modeling the moving boundary based on the mechanical parameters, the movable range of each equipment is determined, which ensures that the movement of the equipment in the digital twin model conforms to the physical reality, avoids the invalidation of the scheme caused by the mismatch between the virtual model and the actual equipment performance, and provides a reasonable spatial constraint for subsequent operation path planning; by combining various rules to form the business constraint rules, the compliance and safety requirements that must be followed in the operation process are determined, which provides a clear judgment standard for the subsequent scheme formulation, avoids illegal operations from the source, and ensures the orderly operation of the operation; by constructing the digital twin model, the digital integration of the yard space structure, the equipment moving range and the business constraint rules is realized, and a virtual mirror consistent with the real yard is formed. The model can not only reflect the real state in real time, but also support operation simulation and optimization, which provides a reliable virtual environment for subsequent loading and unloading scheme formulation, conflict detection and dynamic adjustment, and improves the fine level of operation management.

[0059] In some optional embodiments, S13, determining a coal bulk cargo loading and unloading scheme according to the data set and the digital twin model, comprises: Step 131, dynamically aligning the data set with the digital twin model to eliminate the conflict data in the data set and generating an effective input parameter set.

[0060] In step 131, dynamic alignment refers to the process of real-time comparison of information in the data set with the spatial structure, equipment range, and business constraint rules in the digital twin model, ensuring that the data is consistent with the model logic; conflicting data refers to information in the data set that does not conform to the model rules or is contradictory, such as the same stack position being assigned to two trains at the same time, the planned unloading tonnage exceeding the maximum capacity of the stack position, etc.; the effective input parameter set refers to the data set that meets the model rules and is logically consistent after removing data conflicts (such as the same stack position being assigned to two trains at the same time), providing reliable input for subsequent scheme development.

[0061] In the embodiments of the present application, first, the data set generated in step 12 is imported into the digital twin model through the data interface; then, the model calls built-in business constraint rules (such as stack capacity upper limit, coal type matching requirement) and spatial topological relationship (such as the location of the stack position), and checks each item of information in the data set—such as checking whether "Train C plans to unload coal to stack position A" meets the rules "The coal type of stack position A is consistent with the coal type of Train C", "The current remaining capacity of stack position A ≥ the unloading tonnage of Train C", etc.; if conflicting data (such as the unloading tonnage of Train C exceeding the remaining capacity of stack position A) is found, the data is marked and removed; finally, all data that passes the check is organized into an effective input parameter set. For example, there is a record of "Train D plans to unload 5000 tons of coal to stack position B (remaining capacity 3000 tons)" in the data set, and through dynamic alignment, it is found that the tonnage is conflicting, after removing the record, the remaining data forms an effective input parameter set.

[0062] Step 132, according to the ship berthing window period and train arrival time sequence in the effective input parameter set, combined with the equipment movement path topological relationship in the digital twin model, calculate the allowed execution time interval of the coal bulk cargo loading and unloading task in the current period, generate a task sequence mapping table with timestamp.

[0063] In step 132, the ship berthing window period refers to the time range (such as 10:00-14:00) from berthing to leaving of the ship, which is the time constraint of the loading task; the train arrival time sequence refers to the order of arrival of multiple trains at the port (such as train E arriving at 9:00 and train H arriving at 9:30); the equipment movement path topological relationship refers to the route and distance relationship of the equipment (such as the stacker and the reclaimer) from one location to another recorded in the digital twin model; the allowed execution time interval refers to the time range in which a single task (such as unloading and loading) can start and end, which needs to meet the conditions of equipment availability, connection between previous and subsequent tasks, etc.; the task sequence mapping table with time stamp is a table that records each task (such as "train E unloading" and "ship I loading") and its allowed execution time interval in chronological order. The loading and unloading time sequence Gantt chart is a chart that visually displays the time arrangement of each operation task, which can include constraints to ensure that all trains work at the same time to reduce equipment occupation conflicts; external condition changes include changes in train arrival time, dynamic adjustment of ships, equipment failure, etc.

[0064] In the embodiments of the present application, first, the ship berthing window period (such as ship I 10:00-14:00) and the train arrival time sequence (such as train E arriving at 9:00 and train H arriving at 9:30) are extracted from the effective input parameter set; then, the equipment movement path topological relationship in the digital twin model is called to calculate the movement time of the equipment to complete each task (such as 10 minutes for the stacker to move from the train E unloading point to the pile G); then, the allowed execution time interval of each task is calculated by combining the equipment movement time and the operation time of the task itself (such as 1 hour for train E to unload 6000 tons), for example, the unloading of train E can be carried out at 9:00-10:00 (arriving at 9:00, 1 hour of operation, 10 minutes of movement to the next location, without affecting the subsequent tasks); finally, all tasks and their time intervals are arranged in chronological order to generate a task sequence mapping table with time stamp. For example, the mapping table records "9:00-10:00 train E unloading to pile G" and "10:00-13:00 reclaimer loading ship I from pile G".

[0065] In step 133, based on the coal type demand, flow direction attribute and pile tonnage constraint of the ship predicted in the data set, combined with the preset operation mode, the spatial topological relationship between the pile and the ship cabin is simulated through the digital twin model, and the allocation matrix between the ship cabin, the coal type, the pile and the tonnage is generated within the allowed execution time interval to minimize the equipment movement distance.

[0066] In step 133, in the coal unloading operation, the preset operation mode includes: standby mode, balance mode and high yield mode. The coal type demand refers to the type of coal (such as coal type F) that the ship needs to load; the flow attribute refers to whether the coal transported by the ship is for domestic trade or foreign trade, which determines the division of the operation group; the stack tonnage constraint refers to the maximum storage capacity (such as 27000 tons) of the stack and the current remaining capacity; the preset operation mode includes standby mode (prefer small capacity stack), balance mode (uniformly distribute cargo) and high yield mode (prefer to shorten time); the spatial topological relationship refers to the relative position relationship between the stack and the cabin; the allocation matrix is a structured table recording the correspondence of "cabin-coal type-stack-tonnage", for example "cabin 1-coal type F-stack G-5000 tons".

[0067] Specifically, step 133 can be implemented through the following process, which includes, for example: step a1, according to the flow attribute, divide all cabins of the ship into several independent operation groups, which are foreign trade isolation groups or domestic trade operation groups; step a2, combined with the preset operation mode, call the three-dimensional coordinate data of the stacks in the yard and the geometric center coordinates of the cabins through the digital twin model, and establish a distance mapping table from the center points of each stack to the geometric center points of each cabin of the ship; step a3, according to the coal type demand and the stack tonnage constraint, for each foreign trade isolation group, match the corresponding stack according to the coal type demand, and in the matching process, the tonnage allocation satisfies the sum of the cabin capacities in the group; for the domestic trade operation group, according to the continuity relationship of adjacent cabins, the same coal type is allocated to the position continuous cabin set; step a4, based on the distance mapping table, for a single cabin operation group, accumulate the spatial straight line distance between each cabin in the group and the stack allocated to each cabin, and generate an allocation scheme that meets the coal type demand and tonnage constraint, with the goal of minimizing the accumulated distance value of all cabin operation groups; step a5, structure the allocation scheme to obtain the allocation matrix.

[0068] In the above steps a1~a5, the flow attribute refers to the transportation purpose of the coal, which is divided into foreign trade (for export) and domestic trade (for domestic sales); the independent operation group is a set of ship cabins divided according to the flow attribute, and the ship cabins in the same group need to follow the same operation rules, wherein the foreign trade isolation group requires physical isolation from the domestic trade operation group in space, and the domestic trade operation group can be arranged according to the continuity principle. The preset operation mode includes a high-yield mode (preferably shortening the operation time) and an energy-saving mode (preferably reducing the equipment movement), which affects the weight of distance calculation; the three-dimensional coordinate data refers to the spatial position (X, Y, Z) of the stack position in the digital twin model, wherein Z is the stacking height; the geometric center coordinate of the ship cabin refers to the spatial position (X, Y, Z) of the center point of each ship cabin on the ship; the distance mapping table is a table recording the straight-line distance between the stack position and the ship cabin, which is used for subsequent optimization allocation. The coal type requirement refers to the coal type (such as coal type F) to be loaded in the operation group; the stack tonnage constraint includes the maximum capacity (such as 27000 tons) of the stack position and the current remaining capacity (maximum capacity - already stacked tonnage); the sum of the capacities of the ship cabins in the group refers to the total tonnage that can be loaded by all ship cabins in the same operation group; the continuity relationship between adjacent ship cabins refers to the need to maintain the continuity (such as the same coal type and the same stack source) of the adjacent ship cabins (such as numbers 1 and 2) in the domestic trade operation group. The cumulative distance value refers to the total distance moved by all stackers in a single operation group; minimizing the cumulative distance value is an optimization goal, which reduces the total movement distance of the equipment in the operation by reasonably allocating the corresponding relationship between the stack position and the ship cabin; the allocation scheme refers to the preliminary scheme of specifying which stack position each ship cabin corresponds to and how many tons are allocated. The structured output refers to arranging the allocation scheme into a standard format data according to the fixed level (such as group, ship cabin, coal type, tonnage); the allocation matrix is the result of the structured output, which is a table containing the corresponding relationship of "group identifier-ship cabin number-coal type-stack position-tonnage", which is used to clearly show the allocation logic.

[0069] For example, first, the ship's cabins are divided into independent operation groups (foreign trade isolation group or domestic trade operation group) according to the flow direction attribute; then, combined with the preset operation mode (such as high yield mode), the three-dimensional coordinates of the pile position such as pile position G center point (X1, Y1, Z1) and the geometric center coordinates of the cabin such as cabin 1 center point (X2, Y2, Z2) are called through the digital twin model, and the distance mapping table of the two is calculated and established; then, according to the coal demand (such as coal F) and the pile position tonnage constraint (such as pile position G remaining 5000 tons), the corresponding pile position is matched for each operation group - the foreign trade group strictly matches the coal type, and the domestic trade group allocates the same coal type to consecutive cabins; based on the distance mapping table, the distance between the cabin in the group and the corresponding pile position is accumulated, and the distribution scheme is optimized to minimize the total distance; finally, a unique identifier is assigned to the operation group, the cabin number, coal type, and tonnage are filled layer by layer, the adjacent cabin pile position allocation is adjusted after checking the constraints (if the distance exceeds the threshold, exchange), and the distribution matrix is generated. For example, the 3 cabins of domestic ship I are divided into 1 operation group, the coordinates of pile positions G (remaining 5000 tons) and J (remaining 4000 tons) and cabins 1-3 are called, the distance is calculated after the distribution is "cabin 1-pile position G-3000 tons", "cabin 2-pile position G-2000 tons", "cabin 3-pile position J-4000 tons", and the total distance is minimized.

[0070] In the step a5, the distribution scheme is structured and output to obtain a distribution matrix, which can include the following processes: in step a51, a unique group identifier is assigned to the independent operation group based on the flow direction attribute of the cabin; in step a52, the structured data is filled layer by layer according to the group identifier, the first layer of the structured data is the cabin number sequence arranged according to the physical cabin order of the ship, the second layer is the coal type name associated with each cabin number, and the third layer is the tonnage, which is simultaneously checked for the upper limit of the total capacity of the group and the constraint that the empty pile position cannot load coal; in step a53, the pile position coordinate topology is loaded in the digital twin model, the spatial straight line space of the pile positions allocated to the adjacent cabins in the same operation group is calculated, and if the spatial straight line distance exceeds the set distance threshold, the pile position allocation relationship of the non-adjacent cabins is exchanged; in step a54, based on all the spatial straight line distances and the pile position allocation relationship, the distribution matrix between the cabin, the coal type, the pile position and the tonnage is generated within the allowed execution time interval.

[0071] In the above embodiment, for step a1, first, the flow direction attribute of the ship is extracted from the data set (determined as foreign trade or domestic trade through ship customs information); then, check whether all the ship's cabins need to be split according to the flow direction attribute - if the ship only transports single flow direction coal (such as pure domestic trade), all the ship's cabins are divided into an independent operation group (such as a domestic trade operation group); if the ship transports both foreign trade and domestic trade coal (such as the front half of the ship's cabins loaded with foreign trade coal and the back half loaded with domestic trade coal), the ship's cabins are split into a foreign trade isolation group and a domestic trade operation group according to the flow direction attribute, and a physical isolation space (such as at least 1 empty cabin) is reserved between the two groups; finally, record the cabin numbers included in each group, and complete the division of the independent operation group. For example, ship I is a pure domestic trade ship, including 3 cabins (numbers 1, 2, and 3), which is divided into 1 domestic trade operation group, including cabins 1-3. For step a2, according to the preset operation mode (such as the energy-saving mode needs to consider the distance of equipment movement), the priority of distance calculation is determined; then, through the interface call of the digital twin model, the three-dimensional coordinates of the stack position that meets the coal type requirement in the yard (such as the center point of the stack position G (10, 20, 5), Z is the current stacking height) and the geometric center coordinates of each ship cabin of the ship (such as the center point of cabin 1 (30, 40, 8)) are called; then, the distance between each stack position and each ship cabin is calculated using the spatial straight line distance formula, wherein the distance calculation formula is where (X1, Y1, Z1) is the coordinates of the center of the stack, and (X2, Y2, Z2) is the coordinates of the geometric center of the cabin; finally, all the "stack-cabin-distance" corresponding relationship is sorted into a distance mapping table. For example, in the preset energy-saving mode, the distance from the stack G to the cabin 1 is 25 m, and the distance to the cabin 2 is 27 m, forming a mapping table. For step a3, the coal type requirement of each work group (such as the foreign trade isolation group 01 requiring coal type H) and the sum of the cabin capacities in the group (such as 10000 tons) are extracted; then, for the foreign trade isolation group: the stacks that match the coal type (coal type H) and have a remaining capacity ≥ the sum of the cabin capacities in the group are selected from the digital twin model, if the capacity of a single stack is insufficient, multiple stacks of the same coal type are matched to ensure that the total remaining capacity ≥ the required tonnage; for the domestic trade work group: after selecting the stacks that match the coal type, the same coal type is preferentially assigned to the cabins that are located continuously (such as cabins 1-3), and the sum of the remaining capacities of the stacks must be ≥ the sum of the cabin capacities in the group; finally, the matched stacks and the tonnage range that can be allocated are recorded. For example, the domestic trade work group requires coal type F 8000 tons, matches stack G (remaining 5000 tons) and stack J (remaining 4000 tons), and the total remaining 9000 tons ≥ 8000 tons, satisfying the constraint. For step a4, the distance data of all cabins in a single work group and the matched stacks are extracted from the distance mapping table; then, using the greedy algorithm (preferentially assigning the closest stack to the cabin) or the enumeration method, different "cabin-stack" matching combinations are tried, and the cumulative distance value of each combination is calculated; then, among all the combinations that satisfy the coal type requirement (such as coal type matching) and tonnage constraint (such as stack remaining capacity ≥ allocated tonnage), the combination with the smallest cumulative distance value is selected; finally, according to the combination, the corresponding stack and allocated tonnage of each cabin are determined (which must satisfy the total tonnage in the group = the sum of the cabin capacities), and the allocation scheme is generated. For example, the domestic trade work group has 2 cabins and 2 stacks, and by calculating the cumulative distance of the two matching combinations, the scheme with a smaller total distance (such as cabin 1-stack J, cabin 2-stack G, total distance 45 m) is selected. For step a51, first, the number of independent work groups of the ship and their respective flow attributes (foreign trade or domestic trade) are counted; then, the groups are classified and numbered according to the flow attribute—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 (such as the first foreign trade group is "foreign trade 01", and the second is "foreign trade 02"); finally, the group identifier is associated with the cabin numbers contained in the work group to ensure that each work group has a unique identifier.For example, the ship has 2 foreign trade groups and 1 domestic trade group, which are assigned "foreign trade 01", "foreign trade 02", "domestic trade 01" respectively to ensure the distinction between groups; then, according to step a52, traverse each work group according to the group identifier, fill in the ship cabin numbers contained in the group (arranged in physical order of the ship, such as 1→2→3) in the first layer of structured data; then, fill in the corresponding coal type name of each ship cabin (which needs to be consistent with the coal type matched in step a3) in the second layer; then, fill in the allocated tonnage of each ship cabin (which needs to satisfy the condition that the capacity of a single ship cabin ≤ allocated tonnage ≤ the remaining capacity of the stack position); at the same time, check that "the sum of the tonnages of all ship cabins in the group = the upper limit of the total capacity of the ship cabins in the group" and "all allocated stack positions are non-empty stack positions (have been marked with coal types)", if not, adjust the tonnage or stack position; finally, record the structured data that passes the check. For example, the first layer of the domestic trade 01 group fills 1→2→3, the second layer fills coal type F, and the third layer fills 3000→2000→2000, and the total tonnage is 7000 tons (equal to the total capacity in the group) and the stack position is non-empty; then, according to step a53, load the three-dimensional coordinates of the stack positions allocated to each ship cabin in the same work group in the digital twin model; then, calculate the straight-line distance between the stack positions corresponding to adjacent ship cabins (such as ship cabin 1 and 2) (use the distance formula in step a2); then, compare the calculation result with the set distance threshold (such as 50m), if it exceeds the threshold (such as 60m), find the non-adjacent ship cabins in the group (such as ship cabin 1 and 3), and exchange their stack position allocation relationship; again, calculate the stack position distance of adjacent ship cabins until the stack position distance of all adjacent ship cabins is ≤ threshold; finally, record the adjusted stack position allocation relationship. For example, the stack position distance between adjacent ship cabins 1 (stack position A) and 2 (stack position B) is 70m (exceeds the threshold 50m), exchange the stack positions of ship cabins 1 and 3 (ship cabin 1→stack position C, ship cabin 3→stack position A), and the stack position distance between ship cabins 1 and 2 after adjustment is 40m (≤ threshold); finally, according to step a54, integrate the stack position allocation relationship adjusted in step a53 (such as ship cabin 1→stack position J), coal type information (such as coal type F), tonnage (such as 4000 tons) and group identifier (such as domestic trade 01); then, associate the work group allowed execution time interval (such as 10:00-13:00) determined in step 132; then, arrange the data in the order of "group identifier→ship cabin number→coal type→stack position→tonnage→time interval" to form a standard table form; finally, check whether each data in the matrix is complete and the logic is consistent (such as whether the total tonnage is correct), and output the allocation matrix after confirming that there is no error. 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".

[0072] By the above steps a51~a54, by allocating a unique group identifier, clear distinction and efficient management of different job groups are achieved, providing a convenient identification basis for subsequent data filling, verification and job tracking; by filling data layer by layer and synchronously verifying constraints, the accuracy and compliance of structured data are ensured, laying a data foundation for generating the final allocation matrix; by adjusting the stack position allocation of adjacent cabins, it is ensured that the movement distance of the equipment between adjacent cabins is within a reasonable range, reducing the equipment adjustment time and energy consumption, and improving the job continuity; the generated allocation matrix records the key information required for the job, not only clearly defining the correspondence of "cabin-coal type-stack position-tonnage", but also associating time constraints, providing comprehensive data support for subsequent determination of loading sequence and generation of Gantt chart.

[0073] By the above steps a1~a5, by dividing independent job groups according to flow direction attributes, the external trade and internal trade job areas are strictly distinguished, meeting the business constraints of external trade isolation, laying a foundation for subsequent group allocation of stacks and job arrangement, and ensuring the compliance of the job; by establishing a distance mapping table, the spatial relationship between the stack and the cabin is quantified, providing a data foundation for subsequent allocation optimization targeting "minimizing equipment movement distance", and adapting to the needs of the preset job mode; by matching the stack according to the coal type and tonnage constraints, it is ensured that the coal type demand and loading capacity of the job group meet the requirements, while the continuity of the internal trade group reduces the number of equipment adjustments between different cabins, improving the job efficiency; by optimizing the allocation scheme targeting the minimization of cumulative distance, the total movement distance of the equipment in the job is reduced under the premise of meeting the coal type and tonnage constraints, reducing energy consumption and improving the economy of the job; the allocation matrix records the key information required for the job.

[0074] Step 134, based on the allocation matrix between the cabin, coal type, stack and tonnage, determine the coal type loading sequence and cabin loading sequence.

[0075] In step 134, the volatile characteristic value of the coal type refers to the ability of the coal type to release volatile matter after heating (such as high-volatile coal being prone to spontaneous combustion), which affects the loading priority; the coal type loading sequence refers to the loading sequence of different coal types (such as high-volatile coal first); the priority of the group identifier refers to the job sequence of different job groups (such as external trade group, internal trade group); the in-group loading continuity feature refers to the loading connection requirements of adjacent cabins in the same job group (such as continuous loading to reduce equipment movement); the cabin loading sequence refers to the loading sequence of each cabin in the same job group.

[0076] Specifically, step 134 may include the following process: step b1, according to the volatility characteristic value and distribution matrix of the coal type, the coal types are prioritized to obtain the coal loading order; step b2, according to the priority of the group identifier, the continuity characteristic of the loading within the group, the distribution matrix and the operation mode, the cabin loading order is determined.

[0077] In steps b1 and b2, the operating mode, such as high-volatility mode, prioritizes unloading time. The volatility characteristic value of a coal type refers to the proportion of volatile matter released when heated (e.g., high-volatility coal >30%, medium-volatility coal 10%-30%, and low-volatility coal <10%). Coals with higher volatility are more susceptible to spontaneous combustion and therefore require priority loading. The loading order of coal types refers to the order in which different coal types are loaded (e.g., high-volatility coal is loaded first). The priority of the group identifiers refers to the order in which different operating groups are loaded (e.g., foreign trade groups take precedence over domestic trade groups). The intra-group loading continuity feature requires that adjacent holds within the same operating group be loaded continuously (to minimize equipment movement). The operating mode (e.g., high-volatility mode) influences the sorting logic (e.g., prioritizing efficiency). The loading order of holds refers to the order in which each hold within the same operating group is loaded.

[0078] In this embodiment, all relevant coal types (e.g., coal types F and H) are first extracted from the allocation matrix. The volatility characteristic value of each coal type is then queried, e.g., if coal type F is 20% (medium) and coal type H is 35% (high). The coal types are then sorted from high to low by volatility characteristic value (high > medium > low). If the volatility characteristic values ​​are the same, the coal types are sorted by name or work group priority. Finally, the loading order of the coal types is determined. For example, if the allocation matrix contains coal types H (high volatility) and F (medium volatility), the loading order is H → F. First, the order of the work groups is determined based on the priority of their group identifiers (e.g., foreign trade 01 → domestic trade 01). Then, for each work group, the physical order of the ship's holds (1 → 2 → 3) is prioritized, taking into account the stacking slot distribution in the allocation matrix (e.g., holds 1-3 correspond to the same stacking slot) and the continuity characteristics within the group. If a hold within a work group corresponds to multiple stacking slots, and the physical order would result in frequent equipment movement across stacking slots (e.g., 1 → 3 → 2), the order is adjusted to be based on stacking slot concentration (e.g., loading all holds within the same stacking slot before loading the next stacking slot). Finally, the final loading order is determined based on the preset operation mode (e.g., high-yield mode prioritizes time reduction). For example, if holds 1-3 in the domestic trade 01 group all correspond to stacking slot G, the order is 1 → 2 → 3.

[0079] Through the above steps b1-b2, the loading order of coal types is determined by volatility characteristic values, and high-volatile coal that is prone to spontaneous combustion is prioritized, thereby reducing the safety risks of coal during stacking and transportation and ensuring operational safety. The loading order of the hold is determined by comprehensively considering group priority, continuity, and operation mode, which not only ensures priority processing of important operation groups, but also reduces the movement of equipment between different holds and stacks, thereby improving operational efficiency and continuity.

[0080] Step 135: Input the task sequence mapping table, allocation matrix, coal type loading sequence, and hold loading sequence into the digital twin model, and generate a bulk coal loading and unloading plan displayed in the form of a loading and unloading sequence Gantt chart in combination with the real-time position status of the equipment.

[0081] In step 135, the real-time equipment location status refers to the current location (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, clearly presenting the time schedule of the entire loading and unloading plan.

[0082] In this embodiment, the task sequence mapping table (including task time intervals) from step 132, the allocation matrix (including the ship hold, stack position, and tonnage mappings) from step 133, and the coal type and ship hold loading sequence from step 134 are input into the digital twin model. The model then uses the real-time equipment position status (e.g., a reclaimer is currently in stack position K and needs to move to stack position G) to simulate the execution of each task. For example, task start times are adjusted based on equipment movement time to ensure seamless connection between tasks. Finally, the simulation results are converted into a Gantt chart of the loading and unloading sequence. Tasks such as unloading and loading are color-coded to clearly define the timeframe, equipment used, and corresponding stack positions / ship holds. For example, the Gantt chart clearly shows information such as "9:00-10:00 Stacker M unloads from train E 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)."

[0083] In a specific example, the digital twin model of Coal Terminal A has been constructed through steps 111-115: the stack yard is coded using an 8m×8m grid, and the base grid coordinate system covers an area of ​​600m×400m. The stack spatial topology database records the boundary coordinates and adjacency relationships of 30 stacks (for example, stacks G and J are adjacent). The equipment movement range defines the movable areas of stacker B (tracks X8-X60, Y15) and reclaimer C (activity radius 20m). Business constraint rules include "export coal and domestic coal are separated by 4 grids," "coal types in the same stack cannot be mixed," and "maximum stack capacity is 27,000 tons." Based on this model, the specific process of steps 131-135 is as follows: The data set of coal terminal A contains: train E (coal type F, 5000 tons, expected to arrive at 9:00, planned to be unloaded to stack G), train H (coal type F, 4000 tons, expected to arrive at 9:30, planned to be unloaded to stack G), ship I (need coal type F, 9000 tons, 10:00 berthing). When the data set is dynamically aligned with the digital twin model, the model calls the tonnage constraint of stack G (maximum capacity 8000 tons, current storage 3000 tons, remaining 5000 tons), 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" will cause overcapacity (8000+4000=12000>8000), which is determined as conflicting data. After removing the original plan of H, an effective input parameter set is generated (retaining the plan of train E, train H needs to be reassigned to stack J with coal type F and remaining capacity ≥4000 tons).

[0084] Based on the effective input parameter set, the berthing window period of ship I is 10:00-13:00 (3 hours are needed to complete 9000 tons of loading), train E (5000 tons) arrives at 9:00, and train H (4000 tons, assigned to stack J) arrives at 9:30. The digital twin model calls the equipment movement path topological relationship: the stacker needs 10 minutes to move from train E to stack G and 15 minutes to move from train H to stack J; the unloading efficiency is 1000 tons / 10 minutes (5000 tons need 50 minutes, 4000 tons need 40 minutes). Calculate the allowed execution time interval: train E unloading 9:00-9:50 (arrive at 9:00, 10 minutes of movement, 50 minutes of operation), train H unloading 9:30-10:25 (arrive at 9:30, 15 minutes of movement, 40 minutes of operation), ship I loading 10:00-13:00 (stacker taking materials from G and J, 3 hours to complete 9000 tons). The generated task sequence mapping table records the above tasks and intervals in time.

[0085] Take ship J as an example: ship I is a domestic ship, which needs 9000 tons of coal type F, and has 3 ship holds (No. 1-3). Step a1 divides it into "domestic operation group 01"; step a2 calls the coordinates of pile G (coordinates (10, 20, 5), remaining 5000 tons), pile J (coordinates (15, 20, 5), remaining 4000 tons), and ship hold 1 ((30, 40, 8)), ship hold 2 ((32, 40, 8)), ship hold 3 ((34, 40, 8)), calculates 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 (both are coal type F, total remaining 9000 tons = demand); step a4 selects "ship hold 1-J (4000 tons), ship hold 2-G (3000 tons), ship hold 3-G (2000 tons)" as the target of the minimum cumulative distance (total distance 25.2+30.1+31.9=87.2m); step a5 generates the allocation matrix: "domestic 01, 1, F, J, 4000, 10:00-13:00", "domestic 01, 2, F, G, 3000, 10:00-13:00", "domestic 01, 3, F, G, 2000, 10:00-13:00".

[0086] Ship J transports both foreign trade coal and domestic coal, and has 5 ship holds (1-5): ship holds 1-2, which load foreign trade coal, are divided into "foreign trade isolation group 01", ship holds 4-5, which load domestic coal, are divided into "domestic operation group 01", and ship hold 3 is an isolation empty hold; the final matrix is formed in the order of "group identifier, ship hold number, coal type, pile, tonnage, 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 01, 4, F, M, 3000, 14:00-16:00", and "domestic 01, 5, F, Q, 2000, 14:00-16:00".

[0087] The volatile characteristic value of coal type H is 35% (high), and the volatile characteristic value of coal type F is 20% (medium). According to the order from high to low of the volatile value, the coal loading order is H→F. The operation group priority of ship J is foreign trade 01→domestic 01, and the foreign trade 01 group is loaded in the order of ship hold 1→2 (same pile K), and the domestic 01 group is loaded in the order of 4→5 (reducing equipment movement); the domestic 01 group of ship I is loaded in the order of 1→2→3 (continuous ship hold + same pile priority). The final ship hold loading order is: 1→2 of ship J→1→2 of ship I→3→4→5 of ship J.

[0088] The task sequence mapping table, the allocation matrix, and the loading sequence are input into the digital twin model, combined with the real-time positions of the devices (the stacker-reclaimer M is beside the train E, and the reclaimer N is at the stacking position K), and a Gantt chart is generated: the horizontal axis is 9:00-17:00, and the vertical axis records, in sequence, the tasks of “9:00-9:50 stacker-reclaimer M-unloading from the train E to G”, “9:30-10:25 stacker-reclaimer P-unloading from the train H to J”, “10:00-10:10 reclaimer N moving to J”, “10:10-11:10 reclaimer N-ship I hold 1”, “11:10-12:10 ship I hold 2”, “12:10-13:00 ship I hold 3”, “13:00-13:30 reclaimer N moving to K”, “13:30-15:00 ship J hold 1-2”, and the like, to intuitively show the whole process.

[0089] Through steps 131-135, the application can rely on the digital twin model to realize dynamic verification and conflict elimination of data, and ensure the accuracy and compliance of input parameters; on this basis, in combination with the ship berthing window period, the train arrival time sequence and the device moving path, a scientific task sequence and time interval are generated to avoid task conflicts and waiting; by simulating the spatial topological relationship and optimizing the allocation matrix with the goal of minimizing the device moving distance, and in combination with the coal type characteristics and the operation mode to determine a reasonable loading sequence, the complete scheme is finally presented in the form of a Gantt chart, which not only meets the business constraints such as foreign trade isolation, coal matching, tonnage limitation, but also reduces the device moving energy consumption and operation gap time, improves the efficiency, safety and energy saving of coal handling, at the same time provides a flexible adjustment basis for responding to changes in external conditions, realizes intelligent and fine management of the whole operation process.

[0090] In some optional embodiments, in S14, a first energy consumption value of the coal handling of all coal bulk cargo handling operations in the current period is calculated, and in combination with a second energy consumption value corresponding to the idle time period in the current period, a comprehensive energy consumption index is obtained, including: Step 141, for a single coal bulk cargo handling operation, the single machine handling energy consumption of each device is calculated according to the running state of each device, including belt, stacker-reclaimer, monitoring device and ship loader.

[0091] In step 141, a single coal bulk cargo handling operation refers to a complete unloading, stacking or loading operation (such as unloading of train E to stacking position G); the running state of the device includes running time, power, etc. (such as belt running at 50kW power for 0.5 hours); the single machine handling energy consumption refers to the energy consumed by a single device in a single operation (such as the power consumed by the stacker-reclaimer in a certain loading operation), and the calculation method is the product of the device power and the running time.

[0092] In the embodiment of the present application, first, the devices participating in the operation (belt A, stacker B) are recorded for a single operation (such as train E unloading to the stack G); then, the operating parameters of each device are obtained - the power of the belt A is 50 kW, and the operating time is 0.5 hours; the power of the stacker B is 60 kW, and the operating time is 0.5 hours; then, the single machine loading and unloading energy consumption is calculated according to "energy consumption = power x time", that is, the energy consumption of the belt A is 50x0.5=25 kWh, and the energy consumption of the stacker B is 60x0.5=30 kWh; finally, the single machine loading and unloading energy consumption of each device is recorded. For example, in the ship loading operation, the reclaimer C operates at a power of 40 kW for 3 hours, and the energy consumption is 40x3=120 kWh; the belt D operates at a power of 50 kW for 3 hours, and the energy consumption is 50x3=150 kWh.

[0093] Step 142, accumulate the single machine loading and unloading energy consumption of all devices in all coal bulk loading and unloading operations in the current period to obtain a first energy consumption value of the coal loading and unloading.

[0094] In step 142, the current period refers to a set statistical time period (such as one day or one shift); all coal bulk loading and unloading operations include all unloading and loading operations in the period; and the first energy consumption value is the total energy consumption of all devices in the operation process in the period, which reflects the energy consumption level of the actual operation.

[0095] In the embodiment of the present application, first, the current period (such as 9:00-13:00) is determined; then, the device single machine loading and unloading energy consumption of all single operations in the period is collected - for example, the energy consumption of the belt E in the train E unloading operation is 37.35 kWh, and the energy consumption of the stacker F is 58.1 kWh; the energy consumption of the belt G in the train H unloading operation is 29.8 kWh, and the energy consumption of the stacker H is 45.5 kWh; the energy consumption of the reclaimer I in the ship loading I operation is 120 kWh, and the energy consumption of the belt J is 150 kWh; then, these energy consumption values are accumulated: 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.

[0096] Step 143, according to the standby energy consumption of each device, the time length of the idle time period, and the number of idle time periods, a second energy consumption value corresponding to the idle time period in the current period is determined.

[0097] In step 143, the standby energy consumption refers to the unit time energy consumption when the device is not in operation but in the on state (such as the standby power of the stacker 5 kW); the idle time period refers to the idle time (such as the interval between two operations) in the period without operation, including the waiting time caused by device failure or operation conflict; the time length refers to the duration of a single idle time period (such as 0.5 hours); the number of idle time periods refers to the number of idle times in the period; and the second energy consumption value is the total standby energy consumption of all devices in the idle time.

[0098] In the embodiment of the present application, first, the off-peak time periods in the current period are recorded (such as 9:50-10:00, 11:10-11:20), a total of 2, and the time lengths are 0.17 hours and 0.17 hours respectively; then, the standby power of each device is obtained - the standby power of the stacker is 5kW, the standby power of the reclaimer is 6kW, and the standby power of the belt is 2kW; next, the total standby energy consumption of a single off-peak time period is calculated: (5+6+2) x 0.17≈2.21kWh; then multiplied by the number of off-peak time periods (2 times), the total standby energy consumption = 2.21 x 2≈4.42kWh; finally, the result is determined as the second energy consumption value. For example, 3 off-peak time periods (each 0.5 hours), the total standby power of the device is 10kW, and the second energy consumption value = 10 x 0.5 x 3 = 15kWh.

[0099] In step 144, the first energy consumption value and the second energy consumption value are combined to obtain the comprehensive energy consumption index.

[0100] In step 144, the comprehensive energy consumption index is the sum of the first energy consumption value (operation 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 period, and is used to evaluate the overall energy consumption level and guide subsequent energy saving optimization.

[0101] In the embodiment of the present application, first, the first energy consumption value (such as 440.75kWh) obtained in step 142 and the second energy consumption value (such as 4.42kWh) obtained in step 143 are obtained; then, the two are added: 440.75+4.42≈445.17kWh; finally, the result is determined as the comprehensive energy consumption index of the current period. For example, the first energy consumption value is 350kWh, the second energy consumption value is 15kWh, and the comprehensive energy consumption index = 350+15 = 365kWh.

[0102] For example, in a single operation of train E unloading to pile G, the participating devices are belt E (power 45kW, running 50 minutes) and stacker F (power 70kW, running 50 minutes). When calculating the energy consumption of single machine loading and unloading, first convert the running time to hours: 50 minutes = 50 / 60 ≈ 0.83 hours; then calculate according to "energy consumption = power x time", the energy consumption of belt E = 45 x 0.83 ≈ 37.35kWh, and the energy consumption of stacker F = 70 x 0.83 ≈ 58.1kWh, record the energy consumption values of the two devices. There are 3 operations in a certain period, and the energy consumption of each device is counted respectively: in operation 1 (unloading), the energy consumption of the belt is 30kWh, and the energy consumption of the stacker is 40kWh; in operation 2 (loading), the energy consumption of the reclaimer is 90kWh, and the energy consumption of the belt is 80kWh; in operation 3 (transferring), the energy consumption of the stacker is 50kWh, and the energy consumption of the reclaimer is 60kWh. The total energy consumption of all devices is 350kWh. There are two idle time periods in this period: 10:00-10:10 (10 minutes = 10 / 60 ≈ 0.17 hours) and 12:00-12:30 (30 minutes = 30 / 60 = 0.5 hours). The total standby power of the devices participating in the operation is 15kW (5kW for the stacker, 6kW for the reclaimer, and 4kW for the belt), and the idle energy consumption is calculated according to "idle energy consumption = total standby power x time length": the first idle energy consumption = 15 x 0.17 ≈ 2.55kWh, and the second idle energy consumption = 15 x 0.5 = 7.5kWh; the total energy consumption is 10.05kWh. The first energy consumption value and the second energy consumption value are added to obtain the comprehensive energy consumption index = 350 + 10.05 = 360.05kWh, which is used to analyze the total energy consumption in the period.

[0103] By performing steps 141-144 described above, the present application calculates the energy consumption of each device in a single operation, accurately measures the energy consumption of a single link, provides basic data for subsequent total energy consumption in a period, and helps to identify high energy consumption devices and links. By adding the energy consumption of all devices in the period, the first energy consumption value reflecting the actual operation energy consumption is obtained, which provides core data for evaluating the energy efficiency of the operation in the period. By calculating the standby energy consumption of the idle time, the energy consumption not covered by the first energy consumption value is supplemented, making the energy consumption statistics more comprehensive, and helping to find the energy saving space of the idle link. The comprehensive energy consumption index integrates the total energy consumption of operation and standby, comprehensively reflects the energy consumption in the period, provides data support for adjusting the operation timing and reducing idle time in the next period, and helps to systematically reduce energy consumption.

[0104] Figure 3 A structure diagram of a coal loading and unloading energy consumption dynamic optimization system based on digital twinning provided by the embodiment of the present application, referring to Figure 3, the system may include: Establish module 31, which is used to establish a digital twin model that includes the yard space structure, equipment movement range and business constraint rules.

[0105] The collection module 32 is used to collect planning data of bulk coal loading and unloading operations, equipment status and location data, yard resource occupancy data, and yard constraint data to form a data set.

[0106] Determination module 33 is used to determine the bulk coal loading and unloading plan based on the data set and the digital twin model, and trigger the plan regeneration mechanism when external conditions change. The bulk coal loading and unloading plan includes the correspondence between the ship's cabin and the type of coal, the correspondence between the cabin and the amount of coal, and the loading order of the coal type and the cabin.

[0107] The calculation module 34 is used to calculate the first energy consumption value of coal loading and unloading of all coal bulk loading and unloading operations in the current cycle after the coal bulk loading and unloading plan is completed, obtain a comprehensive energy consumption index, and provide energy-saving guidance information for the next cycle based on the comprehensive energy consumption index.

[0108] A digital twin-based dynamic optimization system for coal loading and unloading energy consumption in an embodiment of the present application is used to implement the aforementioned digital twin-based dynamic optimization method for coal loading and unloading energy consumption. Therefore, the specific implementation method of a digital twin-based dynamic optimization system for coal loading and unloading energy consumption can be seen in the embodiment part of the digital twin-based dynamic optimization method for coal loading and unloading energy consumption in the previous text. Its specific implementation method can refer to the description of the corresponding embodiments of each part, which will not be repeated here.

[0109] The present application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing any of the steps of the above-mentioned method for dynamic optimization of coal loading and unloading energy consumption based on digital twins when executing the computer program.

[0110] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the steps of any of the above-mentioned methods for dynamic optimization of energy consumption of coal loading and unloading based on digital twins.

[0111] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, a read-only memory, a random access memory, a mobile hard disk, a magnetic disk, or an optical disk.

[0112] The embodiment of the present application also provides a computer program product, the computer program product comprising a computer program, the computer program being executed by a processor to realize the steps in any of the coal loading and unloading energy consumption dynamic optimization methods based on digital twinning.

[0113] Those skilled in the art will further appreciate that the functions of the examples described herein-based units and algorithm steps can be implemented using electronic hardware, computer software, or any combination thereof. To clearly illustrate the interchangeability of hardware and software, various examples have been described generally in terms of their functionality, without limitation to the corresponding description in any particular combination of hardware and software. Those skilled in the art will appreciate that various examples can comprise any combination of hardware and software to implement the described functionality.

[0114] The above describes in detail the coal loading and unloading energy consumption dynamic optimization method and system based on digital twinning provided by the present application. The principles and implementation modes of the present application are described herein by applying specific examples, and the above description of the examples is only used to help understand the method of the present application and its core idea. It should be pointed out that, for those skilled in the art, some improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the protection scope of the present application.

Claims

1. A method for dynamic optimization of energy consumption in coal loading and unloading based on digital twin, characterized in that: include: Establish a digital twin model that includes the yard's spatial structure, equipment movement range, and business constraints; Collecting 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, wherein the yard resource occupancy data includes the status of empty stacks; Determining a bulk coal loading and unloading plan based on the data set and the digital twin model, and triggering a regeneration mechanism for the plan when external conditions change, the bulk coal loading and unloading plan including an allocation matrix between the ship's holds, coal types, and tonnages; After the coal bulk loading and unloading plan is completed, the first energy consumption value of coal loading and unloading for all coal bulk loading and unloading operations in the current cycle is calculated, and combined with the second energy consumption value corresponding to the idle time period in the current cycle to obtain a comprehensive energy consumption index. Based on the comprehensive energy consumption index, energy-saving guidance information is provided for the next cycle.

2. The method according to claim 1, characterized in that Determining a coal bulk loading and unloading plan based on the data set and the digital twin model includes: Dynamically aligning the data set with the digital twin model to eliminate conflicting data in the data set and generate a valid input parameter set; Based on the ship berthing window and train arrival sequence in the valid input parameter set, combined with the equipment movement path topology in the digital twin model, the allowed execution time interval of the coal bulk loading and unloading task in the current cycle is calculated, and a task sequence mapping table with a timestamp is generated; Based on the coal type demand, flow direction attributes and stack tonnage constraints predicted by the ship in the data set, combined with the preset operation mode, the spatial topological relationship between the stack and the cabin is simulated through the digital twin model, with the goal of minimizing the equipment movement distance, and generating an allocation matrix between the cabin, coal type, stack and tonnage within the allowed execution time interval; Determine the coal type loading sequence and the ship hold loading sequence based on the allocation matrix between ship holds, coal types, stack positions and tonnage; The task sequence mapping table, the allocation matrix, the coal type loading sequence, and the hold loading sequence are input into the digital twin model, and combined with the real-time position status of the equipment, a coal bulk loading and unloading plan displayed in the form of a loading and unloading timing Gantt chart is generated.

3. The method according to claim 2, characterized in that Based on the coal type demand, flow direction attributes and stack tonnage constraints predicted by the ship in the data set, combined with the preset operation mode, the spatial topological relationship between the stack and the cabin is simulated through the digital twin model, with the goal of minimizing the equipment movement distance. Within the allowed execution time interval, an allocation matrix between the cabin, coal type, stack and tonnage is generated, including: According to the flow attributes, all cabins of the ship are divided into several independent operation groups, and the independent operation groups are foreign trade isolation groups or domestic trade operation groups; Combined with the preset operation mode, the digital twin model calls the three-dimensional coordinate data of the stack positions in the yard and the geometric center coordinates of the cabin, and establishes 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 demand and stack tonnage constraints, for each foreign trade isolation group, the corresponding stack is matched according to the coal type demand. During the matching process, the tonnage allocation satisfies the sum of the cabin capacities within the group. For the domestic trade operation group, the same coal type is allocated to a set of cabins with consecutive positions based on the continuity relationship between adjacent cabins. Based on the distance mapping table, for a single cabin operation group, the spatial straight-line distances between each cabin in the group and the stacking positions allocated to each cabin are accumulated, with the goal of minimizing the accumulated distance values ​​of all cabin operation groups, to generate an allocation plan that meets the coal type requirements and tonnage constraints; The allocation scheme is output in a structured manner to obtain an allocation matrix.

4. The method according to claim 3, characterized in that The allocation scheme is structured and output to obtain an allocation matrix, including: assigning a unique group identifier to the independent operation group based on the flow direction attribute of the cabin; Structured data is populated layer by layer according to the group identifier. The first layer of the structured data is the cabin number sequence arranged according to the physical cabin order of the ship. The second layer is the name of the coal type associated with each cabin number. The third layer is the tonnage. The upper limit of the total cabin capacity in the group and the constraint that empty stacks cannot be loaded with coal are simultaneously verified. Load the stack coordinate topology into the digital twin model and calculate the linear distance between stacks allocated to adjacent cabins within the same work group. If the linear distance exceeds the set distance threshold, the stack allocation relationship of non-adjacent cabins is swapped. Based on all spatial straight-line distances and stack allocation relationships, an allocation matrix among cabins, coal types, stacks and tonnages is generated within the allowed execution time interval.

5. The method according to claim 2, characterized in that The method of determining the coal type loading sequence and the ship hold loading sequence based on the allocation matrix among ship holds, coal types, stack positions and tonnages includes: According to the volatility characteristic values ​​of the coal types and the allocation matrix, the coal types are prioritized to obtain a coal loading order; The order of loading the hold is determined according to the priority of the group identifier, the loading continuity characteristics within the group, the allocation matrix and the operation mode.

6. The method according to claim 1, characterized in that The establishment of a digital twin model that includes the yard's spatial structure, equipment movement range, and business constraint rules includes: Carry out raster coding processing on the storage yard to obtain the reference grid coordinate system; Map the boundary coordinates of the stack positions in the reference grid coordinate system and establish a stack position spatial topology relationship library to characterize the spatial structure of the yard; Based on the mechanical parameters of the equipment, the moving boundary is modeled to obtain the equipment movement range; Combine foreign trade isolation rules, mixed coal risk rules, equipment linkage constraints, and necessary unloading conditions into business constraint rules; Based on the yard's spatial structure, equipment movement range, and business constraint rules, a digital twin model corresponding to the yard is constructed.

7. The method according to claim 1, characterized in that 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 idle time period in the current cycle to obtain a comprehensive energy consumption index, including: For a single bulk coal loading and unloading operation, calculate the loading and unloading energy consumption of each piece of equipment based on its operating status. The equipment includes conveyors, stackers and reclaimers, monitoring equipment, and ship loaders. Accumulate the single-machine loading and unloading energy consumption of all equipment in all coal bulk loading and unloading operations in the current cycle to obtain the first energy consumption value of coal loading and unloading; Determine a second energy consumption value corresponding to the idle time period in the current cycle according to the standby energy consumption of each device, the length of the idle time period, and the number of the idle time periods; The first energy consumption value and the second energy consumption value are combined to obtain a comprehensive energy consumption index.

8. A dynamic optimization system for coal loading and unloading energy consumption based on digital twin, characterized by: include: Establish a module for building a digital twin model that includes the yard's spatial structure, equipment movement range, and business constraint rules; The collection module is used to collect the planning data of bulk coal loading and unloading operations, equipment status and location data, yard resource occupancy data, and yard constraint data to form a data set; a determination module, configured to determine a bulk coal loading and unloading plan based on the data set and the digital twin model, and trigger a regeneration mechanism for the plan when external conditions change, wherein the bulk coal loading and unloading plan includes a correspondence between the ship's holds and the types of coal, a correspondence between the holds and the amount of coal, and a loading sequence of the coal types and the 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, obtain a comprehensive energy consumption index, and provide energy-saving guidance information for the next cycle based on the comprehensive energy consumption index.

9. An electronic device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of a method for dynamic optimization of energy consumption of coal loading and unloading based on digital twins as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, can implement a method for dynamic optimization of energy consumption of coal loading and unloading based on digital twins as described in any one of claims 1 to 7.

Images