Power plant coal yard dynamic management method and related device

Abstract

The invention discloses a power plant coal yard dynamic management method and a related device, and belongs to the technical field of coal yard management. The method comprises the following steps: acquiring three-dimensional data of a coal pile, and generating a three-dimensional point cloud model of the coal pile; performing increase and decrease comparison on the three-dimensional point cloud model of the coal pile and historical data in space to obtain a layering result of a changed part, and constructing a layered coal pile model; coal seam warehousing data, coal yard environment parameters, power plant unit load and unit emission parameters are obtained, and the coal quality attenuation condition, the spontaneous combustion tendency and a load prediction curve are obtained through the coal quality attenuation prediction model; and acquiring future expected coal entering quantity and coal quality, and inputting a pre-constructed multi-target optimization coal piling and taking model by combining the layered coal pile model, the coal quality attenuation condition, the spontaneous combustion tendency and the load prediction curve to obtain an optimized coal piling and taking scheme. The coal yard can be flexibly managed, coal resource utilization is optimized, and the overall operation efficiency of an enterprise is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of coal yard management and relates to a dynamic management method for a power plant coal yard and related devices. Background Art

[0002] In the daily operations of thermal power plants, coal yards are a critical link in coal storage and supply. Their management efficiency and quality directly impact the plant's power generation costs, energy efficiency, and environmental performance. Currently, most thermal power plants use a traditional tiered mixed-stack storage model and a coal storage and retrieval strategy based on manual experience. Inventory management often relies on fixed partitions. Under this approach, coal yard management relies primarily on manual record-keeping and simple monitoring equipment, lacking dynamic response to changes in coal quality and real-time storage and retrieval needs, as well as advance planning.

[0003] This results in extensive inventory management, difficult coal quality management, and low management efficiency; specifically, the following:

[0004] 1) Extensive Inventory Management: Current coal yard inventory management primarily utilizes fixed zoning, stockpiling coal of similar quality. This makes it difficult to effectively address real-time coal consumption fluctuations caused by unit load fluctuations and the uncertainty of coal procurement. Coal storage plans are passive and cannot be adjusted promptly based on actual conditions. This often leads to premature consumption of high-calorific value coal and a backlog of old coal, resulting in low inventory turnover.

[0005] 2) Coal quality management challenges: Existing methods cannot effectively access the coal accumulated at the bottom layer due to the degradation of coal quality and the increase in stored coal temperature, resulting in many problems such as reduced power generation efficiency of the units and increased risk of spontaneous combustion in the coal yard.

[0006] 3) Inefficient Management: Inefficient coal yard inventory management not only resulted in wasted storage space but also failed to cope with significant inventory fluctuations caused by coal price fluctuations. Stacker-reclaimer operations lacked precise planning, resulting in a high rate of ineffective travel, severe equipment wear, increased maintenance costs, and prolonged stacking and reclaiming operations. Summary of the Invention

[0007] The purpose of the present invention is to provide a dynamic management method and related devices for a power plant coal yard, so as to solve the technical problem in the prior art that the coal yard management efficiency is low, which affects the power generation efficiency of the units and the safety of the coal yard.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a method for dynamic management of a coal yard in a power plant, comprising the following steps:

[0010] Collect 3D data of the coal pile and generate a 3D point cloud model of the coal pile;

[0011] By comparing the three-dimensional point cloud model of the coal pile with the historical data in space, the layered results of the changed parts are obtained and the layered coal pile model is constructed;

[0012] Obtain coal seam storage data, coal yard environmental parameters, power plant unit load and unit emission parameters, and obtain coal quality degradation, spontaneous combustion tendency and load prediction curve through coal quality degradation prediction model;

[0013] Obtain the expected future coal quantity and quality entering the plant, combine the stratified coal pile model, coal quality attenuation, spontaneous combustion tendency and load forecast curve, input the pre-built multi-objective optimization stacking and coal loading model, and obtain the optimized stacking and coal loading plan.

[0014] Furthermore, the step of collecting three-dimensional data of the coal pile and generating a three-dimensional point cloud model of the coal pile specifically includes:

[0015] Based on the dust concentration, a laser radar and a millimeter-wave radar are used to perform an all-around scan of the coal pile to obtain three-dimensional data of the coal pile, and a three-dimensional point cloud model of the coal pile is generated based on the three-dimensional data of the coal pile.

[0016] Furthermore, the step of using a laser radar and a millimeter-wave radar to perform an all-round scanning of the coal pile based on the dust concentration specifically includes:

[0017] When the dust concentration sensor detects that the dust concentration is within the standard, the laser radar scans the coal pile;

[0018] When the dust concentration exceeds the standard, the laser radar and millimeter wave radar are activated to scan together;

[0019] The lidar data and millimeter-wave radar data are fused using the Kalman filter method. The specific calculation formula is:

[0020] P_fused=K×P_lidar+(1-K)×P_mmw

[0021] K=e^-(C_dust / C_th)

[0022] Where P_fused represents the spatial coordinate data (x, y, z) generated after fusion processing; P_lidar represents the spatial coordinate data measured by lidar; P_mmw represents the spatial coordinate data measured by millimeter-wave radar; C_dust represents the dust concentration; C_th represents the dust concentration threshold; and K is the fusion weight coefficient.

[0023] Furthermore, the coal seam storage data includes storage time, initial calorific value, initial sulfur content, number of covered coal seams and coal seam depth; the coal yard environmental parameters include coal yard temperature and humidity; the power plant unit load and unit emission parameters include current unit power generation, current NOx emission concentration, current SOx emission concentration, historical load curve, historical emission curve and grid-connected peak and valley power.

[0024] Furthermore, the coal quality attenuation prediction model is an LSTM prediction model based on deep learning.

[0025] Furthermore, the step of obtaining the expected amount and quality of coal entering the plant in the future specifically includes: collating and obtaining plans for transporting coal into the plant by means of ships, railways, and automobiles from the fuel management ERP-related software system and other channels, combining coal purchase contracts and mine laboratory data, and analyzing the expected amount and quality of coal entering the plant in the current and next few days.

[0026] Furthermore, the objective function of the multi-objective optimization coal stacking and loading model is an objective function constructed with the goals of maximizing combustion calorific value, minimizing sulfur emissions, minimizing old coal backlog, minimizing coal storage temperature increase, maximizing coal storage space utilization in the coal yard, and ensuring similar coal quality is stored.

[0027] In a second aspect, the present invention provides a power plant coal yard dynamic management system, comprising:

[0028] A data acquisition module is used to collect three-dimensional data of the coal pile and generate a three-dimensional point cloud model of the coal pile;

[0029] The model building module is used to compare the spatial increase and decrease of the three-dimensional point cloud model of the coal pile with the historical data, obtain the stratification results of the changed part, and build a stratified coal pile model;

[0030] The prediction module is used to obtain coal seam storage data, coal yard environmental parameters, power plant unit load and unit emission parameters, and obtain coal quality degradation, spontaneous combustion tendency and load prediction curve through the coal quality degradation prediction model;

[0031] The scheme generation module is used to obtain the expected future coal quantity and quality entering the plant. It combines the stratified coal pile model, coal quality attenuation, spontaneous combustion tendency and load forecast curve, and inputs the pre-built multi-objective optimization stack coal loading model to obtain the optimized stack coal loading scheme.

[0032] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a method for dynamic management of a coal yard of a power plant when executing the computer program.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for dynamic management of a coal yard of a power plant.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention discloses a dynamic management method and related devices for a power plant coal yard. This method flexibly divides different coal access areas based on unit load changes, coal stockpiling conditions, and coal procurement plans. By real-time monitoring of the coal quality and combustion characteristics of each area, the coal supply to each zone is precisely matched, ensuring an efficient and stable combustion process. This optimizes coal resource utilization and improves the overall operational efficiency of the enterprise. Furthermore, the present invention leverages an advanced coal quality degradation prediction model to accurately analyze the storage duration and composition changes of coal in different areas of the coal yard, implementing a scientific strategy of prioritizing the use of older coal, ensuring stable coal combustion performance, and significantly reducing power generation costs. Furthermore, an intelligent temperature monitoring system is used to monitor the temperature of the coal pile in real time, allowing timely reburning measures to eliminate the root cause of coal spontaneous combustion, ensuring coal storage safety and reducing coal loss and safety hazards caused by spontaneous combustion. Furthermore, the present invention comprehensively streamlines and optimizes the operating procedures of the stacker-reclaimer. By using a multi-objective optimization model for stacker-reclaiming, the coal access path is precisely planned, significantly reducing ineffective travel of the stacker-reclaimer, significantly improving the equipment's operating efficiency, reducing unnecessary wear and failures, and maximizing equipment operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 is a flow chart of the method of the present invention;

[0038] Figure 2 is a schematic diagram of the system of the present invention;

[0039] Figure 3 It is a schematic diagram of the computer device structure of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0041] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0042] See also Figure 1 The embodiment of the present invention discloses a method for dynamic management of a coal yard in a power plant, comprising the following steps:

[0043] S1, collects 3D data of the coal pile and generates a 3D point cloud model of the coal pile;

[0044] High-precision LiDAR and millimeter-wave radar are deployed at appropriate locations around the coal pile. They perform a full-scale scan of the coal pile according to pre-defined parameters. When the dust concentration sensor detects that the dust concentration is within the standard, only the high-precision LiDAR is used to scan the coal pile. When the dust concentration exceeds the standard, the millimeter-wave radar is activated to scan the coal pile simultaneously. After the scan is complete, the collected data is processed using specialized software to generate a 3D point cloud model of the coal pile. The specific calculation formula is:

[0045] P_fused=K×P_lidar+(1-K)×P_mmw

[0046] K=e^-(C_dust / C_th)

[0047] Where P_fused represents the spatial coordinate data (x, y, z) generated after fusion processing; P_lidar represents the spatial coordinate data measured by lidar; P_mmw represents the spatial coordinate data measured by millimeter-wave radar; C_dust represents the dust concentration; C_th represents the dust concentration threshold; and K is the fusion weight coefficient.

[0048] S2, through the three-dimensional point cloud model of the coal pile, compare the increase and decrease in space with the historical data, obtain the stratification results of the changed part, and construct a stratified coal pile model;

[0049] Based on the obtained coal pile point cloud data, the spatial increase and decrease results of the current data compared with the previous data (or a specific data point) are compared to obtain the stratification results of the changed parts during the period. Within each stratum, the space, coal quantity, coal quality, and surface temperature of the coal seam at the corresponding time are recorded. In this way, the coal seam division of the entire coal field is gradually established.

[0050] S3, obtains coal seam storage data, coal yard environmental parameters, power plant unit load and unit emission parameters, and obtains coal quality degradation, spontaneous combustion tendency and load prediction curve through the coal quality degradation prediction model;

[0051] The coal seam storage data includes storage time, initial calorific value, initial sulfur content, number of covered coal seams and coal seam depth; the coal yard environmental parameters include coal yard temperature and humidity; the power plant unit load and unit emission parameters include current unit power generation, current NOx emission concentration, current SOx emission concentration, historical load curve, historical emission curve and grid-connected peak and valley power.

[0052] In this embodiment, the temperature and humidity of the coal yard environment are collected regularly. Among them, the combustible gas sensor, temperature and humidity sensor, and dust concentration sensor are fixedly installed and deployed on the horseway on the top of the closed coal yard and on the sides. The horseway deployment spacing is ≤30m in the vertical direction and ≤50m in the horizontal direction, so as to achieve full coverage of the equipment and collect and transmit data in real time. The infrared thermal imager should be a device with a pan-tilt function, deployed on the horseway on the roof of the closed coal yard, and set up a monitoring screen patrol. It should also be ensured that the preset rotation point of a single device completes at least one infrared temperature measurement within 1 hour. Temperature abnormality trigger mechanism: When it is detected that the area-weighted average temperature of the coal pile area in the preset screen exceeds the preset temperature, the area is immediately locked for continuous monitoring, and the preset point is switched after the next round of patrol begins.

[0053] 1) A deep learning-based LSTM model is used. The input parameters are the coal seam storage time, storage temperature, temperature and humidity of the coal yard since storage, storage quality, and data of other coal seams stacked on the target coal seam since storage. The model is used to predict the current coal quality (calorific value, sulfur content) and temperature of the coal seam and to assess the coal quality degradation and spontaneous combustion tendency.

[0054] The model input parameters are as follows:

[0055]

[0056] The output parameters of the model are:

[0057]

[0058] The intermediate processing process is:

[0059] ① Convert the input parameters into three-dimensional hourly continuous data in the time series, and use B-spline curve difference to fill or replace missing and abnormal data;

[0060] ②Use a bidirectional LSTM neural network and add an attention mechanism to extract features from forward and reverse time;

[0061] ③Use the Adam optimizer and weighted root mean square error as the loss function to converge the model.

[0062] 2) Use time series or time series prediction algorithms based on LSTM and transformer models to predict the unit load in the future. The input parameters are the unit's current load (unit power generation), the unit's current emissions (NOx, SOx), the unit's historical load curve, the unit's historical emission concentration curve, and the local area's historical grid-connected peak and valley power curve.

[0063] The model input parameters are as follows:

[0064]

[0065] The output parameters of the model are:

[0066]

[0067]

[0068] The intermediate processing process is:

[0069] ① Homogenize the data. ② Use a Transformer deep learning network with a multi-head attention mechanism to predict the load for the next 72 hours, with a data time step of 0.25 hours.

[0070] S4, obtains the expected future coal quantity and quality entering the plant, combines the stratified coal pile model, coal quality degradation, spontaneous combustion tendency and load forecast curve, inputs the pre-built multi-objective optimization stack coal loading model, and obtains the optimized stack coal loading plan.

[0071] Based on plans for coal transportation into the plant, including by ship, rail, and truck, obtained from the fuel management ERP-related software system and other channels, combined with coal purchase contracts and mining laboratory data, we analyze the expected coal quantity and quality (including heat content and sulfur content) entering the plant now and in the next few days.

[0072] Multi-objective optimization of the coal stacking and unloading plan: Input parameters include the current coal storage structure in the coal yard, the coal unloading plan required for the expected unit load, coal quality degradation and spontaneous combustion tendency in the coal yard, and coal incoming and outgoing load requirements. The target values are to achieve overall optimization by maximizing combustion calorific value, minimizing sulfur emissions, minimizing the accumulation of old coal, minimizing the rise in coal storage temperature, ensuring similar coal quality is stored in similar stacks, and ensuring high utilization of coal storage space in the coal yard. Each optimization indicator is assigned a weight coefficient, and the coefficient is dynamically adjusted based on the input parameters to achieve a change in the emphasis of multiple objectives under different circumstances.

[0073] See also Figure 2 The embodiment of the present invention discloses a power plant coal yard dynamic management system, which includes a data acquisition module, a model building module, a prediction module and a solution generation module.

[0074] Among them, the data acquisition module is used to collect three-dimensional data of the coal pile and generate a three-dimensional point cloud model of the coal pile; the model construction module is used to compare the spatial increase and decrease of the three-dimensional point cloud model of the coal pile with the historical data, obtain the stratification results of the changed part, and construct a stratified coal pile model; the prediction module is used to obtain coal seam storage data, coal yard environmental parameters, power plant unit load and unit emission parameters, and obtain the coal quality attenuation, spontaneous combustion tendency and load prediction curve through the coal quality attenuation prediction model; the plan generation module is used to obtain the expected future coal quantity and coal quality entering the plant, combine the stratified coal pile model, coal quality attenuation, spontaneous combustion tendency and load prediction curve, input the pre-built multi-objective optimization pile coal extraction model, and obtain the optimized pile coal extraction plan.

[0075] In one embodiment of the present invention, see Figure 3 , provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a power plant coal yard dynamic management method.

[0076] The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the dynamic management method of a power plant coal yard in the above embodiment.

[0077] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0079] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for dynamic management of coal yards in power plants, characterized in that: The following steps are involved: Collect 3D data of the coal pile and generate a 3D point cloud model of the coal pile; By comparing the three-dimensional point cloud model of the coal pile with the historical data in space, the layered results of the changed parts are obtained and the layered coal pile model is constructed; Obtain coal seam storage data, coal yard environmental parameters, power plant unit load and unit emission parameters, and obtain coal quality degradation, spontaneous combustion tendency and load prediction curve through coal quality degradation prediction model; Obtain the expected future coal quantity and quality entering the plant, combine the stratified coal pile model, coal quality attenuation, spontaneous combustion tendency and load forecast curve, input the pre-built multi-objective optimization stacking and coal loading model, and obtain the optimized stacking and coal loading plan.

2. A method for dynamic management of coal yards in power plants according to claim 1, characterized in that: The step of collecting three-dimensional data of the coal pile and generating a three-dimensional point cloud model of the coal pile specifically includes: Based on the dust concentration, a laser radar and a millimeter-wave radar are used to perform an all-around scan of the coal pile to obtain three-dimensional data of the coal pile, and a three-dimensional point cloud model of the coal pile is generated based on the three-dimensional data of the coal pile.

3. A method for dynamic management of coal yards in power plants according to claim 2, characterized in that: The steps of using laser radar and millimeter wave radar to perform all-around scanning of the coal pile based on dust concentration specifically include: When the dust concentration sensor detects that the dust concentration is within the standard, the laser radar scans the coal pile; When the dust concentration exceeds the standard, the laser radar and millimeter wave radar are activated to scan together; The lidar data and millimeter-wave radar data are fused using the Kalman filter method. The specific calculation formula is: P_fused=K×P_lidar+(1-K)×P_mmw K=e^-(C_dust / C_th) Where P_fused represents the spatial coordinate data (x, y, z) generated after fusion processing; P_lidar represents the spatial coordinate data measured by lidar; P_mmw represents the spatial coordinate data measured by millimeter-wave radar; C_dust represents the dust concentration; C_th represents the dust concentration threshold; and K is the fusion weight coefficient.

4. A method for dynamic management of coal yards in power plants according to claim 1, characterized in that: The coal seam storage data includes storage time, initial calorific value, initial sulfur content, number of covered coal seams and coal seam depth; the coal yard environmental parameters include coal yard temperature and humidity; the power plant unit load and unit emission parameters include current unit power generation, current NOx emission concentration, current SOx emission concentration, historical load curve, historical emission curve and grid-connected peak and valley power.

5. The method for dynamic management of coal yard in a power plant according to claim 1, characterized in that: The coal quality attenuation prediction model is an LSTM prediction model based on deep learning.

6. A method for dynamic management of coal yards in power plants according to claim 1, characterized in that: The steps for obtaining the expected amount and quality of coal entering the plant in the future specifically include: collating and obtaining plans for transporting coal into the plant by means of ships, railways, and automobiles from the fuel management ERP-related software system and other channels, and analyzing the expected amount and quality of coal entering the plant currently and in the next few days in combination with the coal purchase contract and mine laboratory data.

7. A method for dynamic management of coal yards in power plants according to claim 1, characterized in that: The objective function of the multi-objective optimization coal stacking model is constructed with the goals of maximizing combustion calorific value, minimizing sulfur emissions, minimizing old coal backlog, minimizing coal storage temperature increase, maximizing coal storage space utilization in the coal yard, and ensuring similar coal quality is stored.

8. A power plant coal yard dynamic management system, characterized in that: include: A data acquisition module is used to collect three-dimensional data of the coal pile and generate a three-dimensional point cloud model of the coal pile; The model building module is used to compare the spatial increase and decrease of the three-dimensional point cloud model of the coal pile with the historical data, obtain the stratification results of the changed part, and build a stratified coal pile model; The prediction module is used to obtain coal seam storage data, coal yard environmental parameters, power plant unit load and unit emission parameters, and obtain coal quality degradation, spontaneous combustion tendency and load prediction curve through the coal quality degradation prediction model; The scheme generation module is used to obtain the expected future coal quantity and quality entering the plant. It combines the stratified coal pile model, coal quality attenuation, spontaneous combustion tendency and load forecast curve, and inputs the pre-built multi-objective optimization stack coal loading model to obtain the optimized stack coal loading scheme.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for dynamic management of a power plant coal yard as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for dynamic management of a power plant coal yard as described in any one of claims 1 to 7 are implemented.

Images