Multi-source and multi-target coal transportation path optimization method for friendly source network and load storage system

Abstract

The application relates to the field of coal transportation scheduling of thermal power plants, and discloses a multi-source and multi-target coal transportation path optimization method for a friendly source network load storage system, which comprises the following steps: S1: static path optimization, based on a directed graph model of a coal transportation network, for each to-be-executed coal transportation task, a static optimal path with the least number of occupied non-repeated nodes is calculated; and S2: dynamic scheduling, the static optimal paths corresponding to all to-be-executed coal transportation tasks are obtained, and based on the nodes passed through by each optimal path in the directed graph, the tasks corresponding to the paths without common nodes are divided into the same execution batch for parallel execution, and the tasks corresponding to the paths with common nodes are divided into different execution batches for serial execution. The operation efficiency and coal blending accuracy of the coal transportation system are significantly improved, and the global resources are conflict-free scheduled in the time dimension.

Description

Technical Field

[0001] This application relates to the field of coal conveying dispatching in thermal power plants, and in particular to a multi-source, multi-objective coal conveying path optimization method for a user-friendly source-grid-load-storage system. Background Technology

[0002] The coal conveying system of a modern thermal power plant is a vast and complex network, interconnected with dozens or even hundreds of coal yards, belt conveyors, transfer stations, T-junctions, and coal crushers. In daily operation, the dispatch center needs to continuously execute the task of transporting specific types of coal from one or more coal yards to designated boiler coal bunkers through a series of equipment, according to the coal blending plan.

[0003] However, due to the large and complex coal conveying systems of modern thermal power plants, existing dispatching methods are insufficient to cope with them. Those skilled in the art will understand that existing dispatching methods have the following four main shortcomings: (1) The complexity of path selection: In the existing technology, there are multiple feasible paths from the starting point to the end point of the coal conveying system. The existing scheduling method is difficult to assess the occupation and impact of the path on system resources and cannot efficiently select the global optimal path.

[0004] (2) The problem of precise synchronization of multiple coal sources: An economical coal blending scheme requires coal to be taken from multiple coal yards in proportion and merged along the way. The existing scheduling method is difficult to coordinate the coal flow of multiple paths to be accurately synchronized before the merging point, which affects the efficiency and accuracy of coal blending.

[0005] (3) Equipment resource conflict and bottleneck problem: Core equipment such as belt conveyors are exclusive resources. The existing scheduling method is prone to conflicts at key nodes when multiple tasks are carried out in parallel, causing "traffic jam" and forming a bottleneck in system efficiency.

[0006] (4) Task execution efficiency and time optimization issues: The existing scheduling method cannot achieve global optimization of task execution order and parallel combination, making it difficult to achieve the goal of the shortest overall time consumption, resulting in low system operation efficiency.

[0007] Therefore, there is an urgent need for a multi-source, multi-objective coal transportation path optimization method that can improve the economic efficiency of coal supply in order to address the shortcomings of existing scheduling methods. Summary of the Invention

[0008] The purpose of this application is to provide a multi-source, multi-objective coal conveying path optimization method for a user-friendly source-grid-load-storage system. Through a hierarchical and progressive global scheduling decision framework, the complex coal conveying process is divided into two processes: static path optimization and dynamic scheduling. This method can effectively solve problems such as complex path selection, asynchronous multi-source merging, and equipment resource conflicts, significantly improve the operating efficiency and coal matching accuracy of the coal conveying system, and achieve conflict-free scheduling of global resources in the time dimension.

[0009] The embodiments of this application disclose a multi-source, multi-objective coal conveying path optimization method for a user-friendly source-grid-load-storage system, comprising the following steps: Step S1: Static path optimization. Based on the directed graph model of the coal conveying network, for each coal conveying task to be executed, calculate the static optimal path with the minimum number of non-repeating nodes. Step S2: Dynamic scheduling and arrangement, obtain the static optimal path corresponding to all coal conveying tasks to be executed, and based on the nodes traversed by each optimal path in the directed graph, divide the tasks corresponding to paths without common nodes into the same execution batch for parallel execution, and divide the tasks corresponding to paths with common nodes into different execution batches for serial execution.

[0010] In another preferred embodiment, the directed graph model of the coal conveying network is constructed in the following manner: Each physical device unit in the coal conveying network is defined as a node in a directed graph; Each node is assigned a node type attribute, and the node type includes at least: coal yard node, belt conveyor node, merging node, and coal bunker node; Each node is assigned a quantitative attribute, which includes at least the node's maximum transport capacity and the node's current state; for coal yard nodes, the quantitative attribute also includes the preparation time cost for switching between different coal piles. The physical connection for unidirectional coal transport in a coal conveying network is defined as a directed edge in a directed graph.

[0011] In another preferred embodiment, step S1, which calculates the static optimal path for each coal conveying task to be executed, specifically includes: For a task that requires taking coal from multiple source coal yards and transporting it to a target coal bunker, a depth-first search algorithm is used to search for all feasible paths from each source coal yard to the target coal bunker in the directed graph, and then combine them to obtain multiple candidate paths. Construct a cost function to evaluate the operating cost of each candidate path; The candidate path with the minimum cost function value is determined as the optimal path for the task.

[0012] In another preferred embodiment, the cost function is the total number of non-repeating conveyor belt nodes occupied by all sub-paths in the candidate path, specifically expressed as: in, This refers to the total number of non-repeating conveyor belt nodes occupied by all sub-paths in the candidate path. The candidate path is the first Strip path The set of all belt conveyor nodes included. This represents the total number of all sub-paths included in the candidate path. This indicates merging all belt conveyor nodes in the candidate paths and removing duplicates. This indicates the number of elements in the set.

[0013] In another preferred embodiment, after determining the optimal path, a multi-source coal supply synchronization step is also included, specifically: Determine the first common intersection node of all sub-paths in the candidate path as the rendezvous point; Calculate the total time taken for each sub-path to reach the confluence point from the corresponding source coal yard. The total time is equal to the sum of the preparation time cost and transportation time of the source coal yard for that sub-path. The maximum value among the total times of all sub-paths is determined as the synchronization reference time; For each sub-path, calculate its delayed start time, which is equal to the synchronization reference time minus the total time consumed by the sub-path; Based on the aforementioned delayed startup time, startup instructions for each sub-path are generated.

[0014] In another preferred embodiment, the transportation time is calculated in the following manner: Transportation time = amount of coal to be transported in this sub-path / minimum transportation capacity of all nodes in this sub-path.

[0015] In another preferred embodiment, step S2, which involves dividing the task into different execution batches, specifically includes: Task priority sorting steps: Based on the planned coal transportation volume of each task, sort all coal transportation tasks to be executed in descending order to obtain the priority order of the coal transportation tasks to be executed. Batch scheduling steps: According to the priority order, the coal conveying tasks to be executed are divided into multiple execution batches in sequence; wherein, the node sets contained in the optimal paths corresponding to any two different tasks within the same execution batch are mutually exclusive.

[0016] In another preferred embodiment, the step of calculating the total duration of the plan is further included, specifically: Calculate the total duration of a single task. The total duration of a task is equal to the maximum value of the preparation time cost of all source coal yards for that task plus the transportation time of that task. The transportation time is equal to the planned coal transport volume of that task divided by the minimum transportation capacity of all nodes on the optimal path of that task. Calculate the batch duration of an execution batch. The batch duration is equal to the maximum of the total task duration of all tasks within the batch. Calculate the total duration of the entire coal transportation plan. The total duration is equal to the sum of the durations of all executed batches.

[0017] In another preferred embodiment, a depth-first search algorithm is used in step S1; and a batch partitioning algorithm based on priority sorting and resource exclusivity constraints is used in step S2.

[0018] In another preferred embodiment, the method is applied to coal dispatching of thermal power plants in a friendly source-grid-load-storage system.

[0019] The embodiments of this application also disclose a multi-source, multi-objective coal conveying path optimization device for a user-friendly source-grid-load-storage system, comprising: Memory, used to store computer-executable instructions; and, A processor, coupled to the memory, is configured to implement the steps of the method described above when executing the computer-executable instructions.

[0020] Embodiments of this application also disclose a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps in the method described above.

[0021] Embodiments of this application also disclose a computer program product including computer-executable instructions that, when executed by a processor, implement the steps in the method described above.

[0022] The main differences and effects of the implementation method of this application compared with the prior art are as follows: By adopting a hierarchical and progressive global scheduling decision framework, the complex global coal transportation scheduling problem is divided into two processes: static path optimization and dynamic scheduling. This can effectively solve problems such as complex path selection, asynchronous multi-source merging, and equipment resource conflicts, significantly improving the operating efficiency and coal matching accuracy of the coal transportation system, and achieving conflict-free scheduling of global resources in the time dimension.

[0023] Furthermore, by abstracting the physical network into a mathematical model and using the DFS algorithm for searching, the globally optimal path can be selected efficiently and scientifically from a massive number of feasible paths.

[0024] Furthermore, through an innovative cost function, the optimization objective is transformed from the traditional "shortest distance" or "fastest time" to "lowest operational complexity".

[0025] Furthermore, the global optimal path is efficiently selected through static path optimization, and precise synchronization of multi-source merging is achieved.

[0026] Furthermore, in the static path optimization process, for the problem of blending multiple coals, by calculating the convergence point, branch bottleneck capacity, and synchronization reference time, the delayed start command for different coal flows can be accurately calculated. This enables precise control of coal flows starting from different coal yards, ensuring that they arrive at the convergence point at the same time in strict accordance with the requirements of the coal blending scheme. This greatly improves the accuracy and efficiency of coal blending, and enables coordination in time and space dimensions, scientifically and accurately achieving the blending of multiple coals.

[0027] Furthermore, dynamic scheduling methods are used to address equipment resource conflicts and bottlenecks in the time dimension, thereby improving task execution efficiency and optimizing scheduling time.

[0028] Furthermore, in the dynamic scheduling process, the batch scheduling strategy transforms the complex resource conflict problem into a simple set intersection judgment problem, and arranges non-conflicting tasks to be executed in parallel in the same batch, thereby maximizing the utilization of system resources, avoiding "traffic jams", and breaking through efficiency bottlenecks.

[0029] Furthermore, the entire framework is an optimization process that moves from "individual optimization" to "global coordination." First, static optimization finds the path with the least resource consumption for each task, creating conditions for global parallelism. Then, through dynamic scheduling, tasks are arranged into as many parallel batches as possible, achieving global optimization of task execution order and parallel combination, thereby significantly improving the operational efficiency of the entire coal conveying system. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating a multi-source, multi-objective coal conveying path optimization method for a user-friendly source-grid-load-storage system according to an embodiment of this application. Figure 2 This is a schematic diagram of the overall process of optimizing a coal conveying system according to a preferred embodiment of the present application. Figure 3 yes Figure 2 The diagram shows a sub-process A: a flowchart of the optimal path search for a single task. Figure 4 yes Figure 2 The diagram shows a sub-process B: a flowchart of global task scheduling and arrangement. Detailed Implementation

[0031] In the following description, numerous technical details are presented to facilitate the reader's understanding of this application. However, those skilled in the art will understand that the technical solutions claimed in the claims of this application can be implemented even without these technical details and with various variations and modifications based on the following embodiments.

[0032] Explanation of some concepts: 1. Source-Grid-Load-Storage: This refers to a new power system operation mode that uses advanced communication, information, and control technologies to coordinate and interact with the four relatively independent links in the traditional power system. The source refers to the power generation side, including traditional coal-fired power plants (i.e., thermal power plants), gas-fired power plants, and new energy sources such as wind farms, solar photovoltaic power stations, hydropower stations, and nuclear power plants. The grid refers to the power grid side, the power transmission and distribution network, including substations, transmission lines, and distribution networks. It acts like the "highway" of the power system, responsible for safely and stably transmitting electricity generated by the power generation side to users. The load refers to the load side, the electricity consumers, including factories, commercial buildings, residential homes, and charging piles, passively receiving and using electricity from the grid. The storage side refers to the energy storage side, the electricity storage devices, including pumped-storage hydroelectric power stations, electrochemical energy storage (batteries), and flywheel energy storage. It acts like a huge "power warehouse" or "power bank." In traditional systems, energy storage plays a weak role, but in new systems, it is used to smooth out fluctuations in new energy sources, storing electricity during periods of low demand and discharging it during periods of high demand, thus playing a regulatory role.

[0033] The core of a power source-grid-load-storage system lies in enabling these four components to interact and collaborate, rather than operating in isolation, to jointly address challenges. This collaboration makes the entire power system safer and more stable (coping with fluctuations), more efficient (reducing waste), and cleaner (accommodating more renewable energy sources).

[0034] The technical solution of this application is applicable to the coal conveying system of thermal power plants in the aforementioned friendly source-grid-load-storage system.

[0035] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0036] The embodiments of this application relate to a multi-source, multi-objective coal conveying path optimization method for a user-friendly source-grid-load-storage system. Figure 1 This is a flowchart illustrating the multi-source, multi-objective coal conveying path optimization method for a user-friendly source-grid-load-storage system.

[0037] Specifically, such as Figure 1 As shown, the multi-source, multi-objective coal conveying path optimization method for a user-friendly source-grid-load-storage system includes the following steps: In step S1, static path optimization is performed. Based on the directed graph model of the coal conveying network, a static optimal path with the minimum number of non-repeating nodes is calculated for each coal conveying task to be executed.

[0038] In this embodiment, preferably, the directed graph model of the coal conveying network is constructed in the following manner: Each physical device unit in the coal conveying network is defined as a node in a directed graph; Each node is assigned a node type attribute, and the node type includes at least: coal yard node, belt conveyor node, merging node, and coal bunker node; Each node is assigned a quantitative attribute, which includes at least the node's maximum transport capacity and the node's current state; for coal yard nodes, the quantitative attribute also includes the preparation time cost for switching between different coal piles. The physical connection for unidirectional coal transport in a coal conveying network is defined as a directed edge in a directed graph.

[0039] In this embodiment, the physical coal conveying network is abstracted into a precise mathematical model—a directed graph. Directed graph A set of nodes and a set of directed edges Composition, denoted as = Through this directed graph model, any complex coal conveying network can be transformed into a standardized mathematical object that can be analyzed and computed by algorithms.

[0040] In step S1 above, the static optimal path is calculated for each coal conveying task to be executed, specifically including: For a task that requires taking coal from multiple source coal yards and transporting it to a target coal bunker, the depth-first search (DFS) algorithm is used to search all feasible paths from each source coal yard to the target coal bunker in the directed graph and combine them to obtain multiple candidate paths. Construct a cost function to evaluate the operating cost of each candidate path; The candidate path with the minimum cost function value is determined as the optimal path for the task.

[0041] In this embodiment, by abstracting the physical network into a mathematical model and using the DFS algorithm for searching, the globally optimal path can be selected efficiently and scientifically from a massive number of feasible paths.

[0042] In this embodiment, preferably, the cost function is the total number of non-repeating conveyor belt nodes occupied by all sub-paths in the candidate path, specifically expressed as: in, This refers to the total number of non-repeating conveyor belt nodes occupied by all sub-paths in the candidate path. The candidate path is the first Strip path The set of all belt conveyor nodes included. This represents the total number of all sub-paths included in the candidate path. This indicates merging all belt conveyor nodes in the candidate paths and removing duplicates. This indicates the number of elements in the set.

[0043] In other words, the candidate path It is by A set of independent sub-paths Each sub-path The starting point is the source coal yard. The destination is the target coal bunker. ,in, From 1 to Integers.

[0044] The ultimate goal of static path optimization is to find the path from all possible candidate paths that satisfies the aforementioned cost function, without considering time constraints or inter-task conflicts. The solution that is minimized .in, .

[0045] In this embodiment, the optimization objective is transformed from the traditional "shortest distance" or "fastest time" to "lowest operational complexity" through the innovative cost function described above.

[0046] In other words, the optimal path in this implementation is not the shortest distance or the fastest time in the traditional sense, but rather the path with the lowest operational complexity.

[0047] Furthermore, preferably, the lowest operational complexity means having the fewest total number of core equipment such as belt conveyors.

[0048] In this embodiment, the example that minimizes the total number of belt conveyors used is preferred. Of course, in other embodiments, other exclusive resources can also be used as examples, and it is not limited to belt conveyors.

[0049] The cost function described above aims to measure the operational simplicity of a candidate path, i.e., the fewer devices used, the lower the system complexity, and the fewer potential points of failure and operational steps.

[0050] Furthermore, preferably, after determining the optimal path, the method also includes a multi-source coal supply synchronization step, specifically including: Determine the first common intersection node of all sub-paths in the candidate path as the rendezvous point; Calculate the total time taken for each sub-path to reach the confluence point from the corresponding source coal yard. The total time is equal to the sum of the preparation time cost and transportation time of the source coal yard for that sub-path. The maximum value among the total times of all sub-paths is determined as the synchronization reference time; For each sub-path, calculate its delayed start time, which is equal to the synchronization reference time minus the total time consumed by the sub-path; Based on the aforementioned delayed startup time, startup instructions for each sub-path are generated.

[0051] The transportation time is calculated in the following way: Transportation time = amount of coal to be transported in this sub-path / minimum transportation capacity of all nodes in this sub-path.

[0052] In other words, when coal is drawn from different coal yards and mixed, it is necessary to ensure that coal from all sub-paths arrives at the mixing point at the same time. A static path optimization method is used to efficiently select the globally optimal path and achieve precise synchronization of multi-source coal merging.

[0053] In the static path optimization process, for the problem of blending multiple coals, by calculating the convergence point, branch bottleneck capacity, and synchronization reference time, the delayed start command for different coal flows can be accurately calculated. This enables precise control of coal flows starting from different coal yards, ensuring that they arrive at the convergence point at the same time in strict accordance with the requirements of the coal blending scheme. This greatly improves the accuracy and efficiency of coal blending and enables coordination in time and space dimensions, scientifically and accurately achieving the blending of multiple coals.

[0054] Then proceed to step S2, dynamically schedule and obtain the static optimal path corresponding to all coal conveying tasks to be executed, and based on the nodes traversed by each optimal path in the directed graph, divide the tasks corresponding to paths without common nodes into the same execution batch for parallel execution, and divide the tasks corresponding to paths with common nodes into different execution batches for serial execution.

[0055] In this embodiment, preferably, step S2 above, which involves dividing the task into different execution batches, specifically includes: Task priority sorting steps: Based on the planned coal transportation volume of each task, sort all coal transportation tasks to be executed in descending order to obtain the priority order of the coal transportation tasks to be executed. Batch scheduling steps: According to the priority order, the coal conveying tasks to be executed are divided into multiple execution batches in sequence; wherein, the node sets contained in the optimal paths corresponding to any two different tasks within the same execution batch are mutually exclusive.

[0056] Furthermore, preferably, step S2 above also includes: calculating the total planned duration, specifically including: Calculate the total duration of a single task. The total duration of a task is equal to the maximum value of the preparation time cost of all source coal yards for that task plus the transportation time of that task. The transportation time is equal to the planned coal transport volume of the task divided by the minimum transportation capacity of all nodes on the optimal path of that task. Calculate the batch duration of an execution batch. The batch duration is equal to the maximum of the total task duration of all tasks within the batch. Calculate the total duration of the entire coal transportation plan. The total duration is equal to the sum of the durations of all executed batches.

[0057] In dynamic scheduling, a batch scheduling strategy transforms complex resource conflict problems into simple set intersection judgment problems, allowing non-conflicting tasks to be executed in parallel within the same batch. This maximizes system resource utilization, avoids congestion, and breaks through efficiency bottlenecks. Dynamic scheduling methods address device resource conflicts and bottlenecks in the time dimension, improving task execution efficiency and optimizing scheduling time.

[0058] In this embodiment, preferably, a depth-first search algorithm is used in step S1; and a batch partitioning algorithm based on priority sorting and resource exclusivity constraints is used in step S2.

[0059] This process will then end.

[0060] It should be noted that the multi-source, multi-objective coal conveying path optimization method proposed in this application for a friendly source-grid-load-storage system is applied to the coal conveying dispatching of thermal power plants in a friendly source-grid-load-storage system.

[0061] In summary, this multi-source, multi-objective coal conveying path optimization method for a user-friendly power-grid-load-storage system couples a direct-fired pulverized coal boiler system with a coal-water slurry energy storage system. By replacing the traditional pulverized coal storage scheme with the coal-water slurry energy storage system, and by utilizing the safety characteristics and precise liquid metering advantages of coal-water slurry, it can effectively improve the rapid load-increasing capacity of thermal power units and reduce safety risks.

[0062] To better understand the technical solution of this application, a preferred embodiment will be described below. The details listed in this embodiment are mainly for ease of understanding and are not intended to limit the scope of protection of this application.

[0063] The purpose of this embodiment is as follows: (1) The global optimal path is efficiently selected through the static path optimization method, and the precise synchronization of multi-source merging is achieved.

[0064] (2) Solve the problem of equipment resource conflict and bottleneck in the time dimension by using dynamic scheduling method, improve task execution efficiency and optimize scheduling time.

[0065] The specific steps of this embodiment are as follows: 1. Mathematical Modeling of Coal Conveying Networks To design the algorithm, in this embodiment, the physical coal conveying network is first abstracted into a precise mathematical model—a directed graph.

[0066] A directed graph A set of nodes and a set of directed edges Composition, denoted as = .

[0067] (1) Node set Each node in a directed graph Each of these represents an actual physical device unit.

[0068] (2) Node type: Nodes are assigned a type attribute, such as (coal yard) (belt conveyor) (Meeting point) (Coal bunker), etc.

[0069] (3) Node attributes: Each node It possesses key quantitative attributes, including: Maximum transport capacity of a node (unit: tons / hour).

[0070] The current state of the node. .

[0071] For coal yard nodes, this represents the preparation time cost (in hours) for switching between different coal piles.

[0072] (4) Set of directed edges Each directed edge in the graph This represents a physical connection, indicating that coal can be transported from the equipment node. Direct and seamless transmission to device nodes .

[0073] The topology of the physical coal yard is abstracted using directed graphs. Different equipment in the physical coal yard is modeled using different node types, and the mechanism of unidirectional coal flow in the coal yard is modeled using directed edges.

[0074] This model allows any complex coal transportation network to be transformed into a standardized mathematical object that can be analyzed and computed by algorithms.

[0075] 2. Algorithm Establishment To achieve intelligent scheduling of complex coal conveying networks, this embodiment constructs a hierarchical and progressive decision-making framework. Its core idea is to decompose the complex global scheduling problem into two related but independent sub-problems for solution: (1) Static path optimization: This stage does not consider the time dimension or conflicts between tasks. Its sole objective is to find one or a set of optimal paths in the current coal transportation network topology for an independent coal transportation task. Here, "optimal" does not mean the shortest distance in the traditional sense, but rather the lowest operational complexity as defined by us. This part uses the DFS static path optimization algorithm, see section 2.1 for details.

[0076] (2) Dynamic scheduling: After the optimal paths for all tasks are determined, this stage introduces the time dimension. It treats all tasks as processes that need to compete for limited device resources, and resolves all potential resource conflicts through an efficient scheduling algorithm, generating a time-optimal global execution plan. This part uses dynamic scheduling and conflict resolution strategies, see 2.2 for details.

[0077] This divide-and-conquer strategy greatly reduces the complexity of solving the problem, enabling the system to generate high-quality scheduling solutions in a short time.

[0078] 2.1 DFS Static Path Optimization Algorithm The core of this stage is to handle a single multi-source coal transportation task, as shown in the diagram. Find the optimal path combination.

[0079] Path definition: A path starting from the origin To the finish line Simple path It is a sequence of nodes. ,in And all nodes in the sequence are unique. The set of nodes contained in the path is denoted as . .

[0080] Path search: The system uses the depth-first search (DFS) algorithm to recursively traverse the directed graph. Find the source of each task: the coal yard. To the target coal bunker The set of all possible simple paths.

[0081] Multi-source path combination and cost function: For a set of coal yards that need to be sourced from the source... A complete path solution for coal extraction tasks. It is by A set of independent paths Each path The starting point is The destination is the target coal bunker. .

[0082] This embodiment defines a cost function for a path solution as the total number of belt conveyors it occupies. This function aims to measure the operational simplicity of the solution; that is, the fewer devices used, the lower the system complexity, and the fewer potential points of failure and operational steps.

[0083] in Indicates the number of elements in the set. The number of belts occupied by the scheme. Let a given path contain the set of all belts. This indicates merging all paths of the belt and removing duplicates.

[0084] Optimization objective: The ultimate goal of static path optimization is to find the path combination that satisfies the above cost function from all possible path combinations. The solution that is minimized . This embodiment constructs a static path optimization objective function. Unlike the traditional method where different conveyor belts have the same cost coefficient, in this embodiment, due to the different transport capacities of different coal conveyor belts in the coal yard, the cost of different nodes has different weights.

[0085] It is worth noting that when mixing coal from different coal yards, it is necessary to ensure that coal from each path arrives at the mixing point at the same time. Suppose a coal mixing task requires coal to be collected from the source coal yard... Coal extraction, its optimal path solution This has been determined. The algorithm performs the following steps: (1) Determine the rendezvous point. First, the algorithm needs to find the first common intersection node of all paths, i.e., the rendezvous point. This node is the coal mixing point.

[0086] in All paths The node on the path is the first one on the path.

[0087] (2) Calculate the total time taken for each branch to reach the confluence point. Next, the algorithm will independently calculate the time taken for each coal stream from its originating coal yard. Arrive at the meeting point Total time required.

[0088] in, For branch paths, represent complete paths. From the starting point to the meeting point Subpaths; Branch bottleneck capacity refers to the minimum transport capacity of all device nodes on the branch path. For branch transportation time, the amount of coal that needs to be transported by that branch. Apart from its bottleneck capabilities; This indicates the time it takes for a coal yard reclaimer to move to the coal pile it has reclaimed. The total time spent on a branch is equal to the preparation time for that branch, i.e., the time spent at the source coal yard. Switching time Add the branch transportation time.

[0089] (3) Determine the synchronization reference time. The preparation and transportation work of all branches must be synchronized with the branch that takes the longest time. This longest time is called the synchronization reference time. .

[0090] (4) Calculate the delayed start instructions for each branch. To ensure that all branches with shorter execution times are synchronized with the longest branch, they must be delayed before starting. Branches need to delay startup time The calculation is as follows: Execute command: For the branch that takes the longest time (its =0), the operation command is to start immediately.

[0091] For other branches, the operation instruction is to wait. Restart after an hour.

[0092] 2.2 Dynamic Scheduling and Conflict Resolution Strategies The optimal path combination was determined for each task. Then, the dynamic scheduling module intervenes to resolve the resource competition problem of all tasks in the time dimension.

[0093] Task Prioritization: To improve scheduling efficiency and solution quality, all pending coal conveying tasks are prioritized. It will be based on its planned delivery volume Sort in descending order. This is an efficient heuristic strategy that prioritizes the most time-consuming and critical tasks that impose the strongest constraints on the system, providing greater flexibility for the scheduling of numerous subsequent smaller tasks.

[0094] Batch scheduling: The scheduler divides all tasks into batches using an iterative approach. An orderly execution batch The core of the allocation is the resource exclusivity constraint: within the same batch Any two different tasks within and The sets of device nodes they occupy must be disjoint, that is: , , in It is a task The set of all nodes included in the optimal path solution. It is a task The set of all nodes included in the optimal path solution. It refers to a specific batch of executions.

[0095] This embodiment determines whether different optimal coal conveying paths can be parallelized in time by judging whether there is an intersection between the directed graphs corresponding to different optimal coal conveying paths. It abstracts the real-world parallel problem into judging whether directed graphs have an intersection.

[0096] Time calculation model: single task Total duration It consists of preparation time, such as coal pile switching time, and actual transportation time. Among these, Actual shipping time: in This refers to finding the optimal path for the task. Among all device nodes, the transportation capacity The lowest one. This lowest capacity is the bottleneck of the entire route, and it determines the actual transport speed (tons / hour). The planned delivery volume for a specific task.

[0097] Total task duration: in, This indicates the time it takes for the reclaimer to move to the coal pile it is reclaiming. This refers to situations where a task requires coal from multiple source coal yards. For coal extraction, the preparation time depends on the longest preparation time spent switching to the coal yard. Since the preparation work for each coal yard, such as the mobile stacker-reclaimer, can be carried out in parallel, this project only needs to focus on the coal yard that is prepared last.

[0098] Batch duration: one batch The execution time depends on the longest-running task in the batch, because tasks within a batch are executed in parallel.

[0099] in This indicates the execution time of a specific batch. This indicates the longest task duration in this batch.

[0100] Total planned duration: The total time required for the entire coal transportation plan. It is the sum of the durations of all batches.

[0101] The optimization objective of a scheduling system is to implicitly minimize the total execution time. .

[0102] In this embodiment, the core process of the entire intelligent coal conveying scheduling system is a decision-making process that moves from individual optimization to global coordination and optimization. Figure 2 This is a schematic diagram of the overall process of optimizing a coal conveying system according to this embodiment.

[0103] like Figure 2 As shown, the system first runs the static path optimization algorithm. In this part, the software traverses each coal conveying task and inputs the coal conveying task into "Subprocess A: Optimal Path Optimization of a Single Task" to obtain the static optimal path for each coal conveying task. Figure 3 This is a flowchart illustrating the optimal path search process for a single task, which is a sub-process A.

[0104] Afterwards, the system runs the dynamic scheduling algorithm, inputting all tasks, along with their optimal paths, into "Subprocess B: Global Task Scheduling and Planning", and obtains a global coal conveying plan with time sequence through Subprocess B. Figure 4 This is a flowchart of sub-process B: global task scheduling and arrangement.

[0105] like Figure 3 As shown, in subprocess A, for any coal conveying task, all possible paths are searched using the DFS algorithm. Then, traverse all feasible paths and calculate their costs. Find the lowest-cost feasible path .

[0106] Obtain the optimal coal conveying path Then, the startup delay is calculated, and finally a task plan containing the optimal path, cost, and timing is output.

[0107] After the main program completes the traversal, it should obtain a solution based on the optimal coal conveying scheme. The solution space constituted .like Figure 4 As shown, in sub-process B, the first step is to read... According to the planned transport volume Sort in descending order, then initialize an empty batch. Iterate through all currently unscheduled tasks and add them to non-conflicting tasks in sequence to obtain a full batch. Coal conveying schemes within a batch can be executed in parallel. Then, the solution space is traversed. For any remaining tasks in the solution space, initialize a new batch, iterate through all currently unscheduled tasks, and add non-conflicting tasks one by one until there are no remaining tasks in the solution space. Output the batch combination with time sequence. This combination constitutes the complete execution plan.

[0108] The aforementioned hierarchical and progressive global scheduling decision framework innovatively divides the complex coal transportation optimization process into two processes: static path optimization and dynamic scheduling. Specifically, the optimal path in the static space is first solved using graph theory, and then the optimal combination of scheduling methods in the time series is solved using the method of "task priority ranking + batch scheduling".

[0109] Therefore, compared with the prior art, the technical solution of this embodiment has the following three main advantages: (1) Improved the efficiency of coal transportation and distribution process: Compared with traditional manual coal conveying and distribution methods, this method can efficiently find the optimal coal conveying path, improving the efficiency and scientific nature of coal conveying and distribution.

[0110] (2) The coal conveying and distribution routes have been optimized: This method can quickly find the optimal coal conveying path. Furthermore, for the problem of blending multiple types of coal, this method can achieve synergy in both time and space dimensions, and scientifically and accurately achieve the blending of multiple types of coal.

[0111] (3) Parallel operation of different coal blending processes was achieved: This method can automatically schedule different coal conveying tasks to run in parallel over time without interfering with each other, greatly improving the efficiency of the coal conveying process.

[0112] Accordingly, embodiments of this application also provide a multi-source, multi-objective coal conveying path optimization device for a user-friendly source-grid-load-storage system, including a memory for storing computer-executable instructions and a processor; the processor is used to implement the steps in the above-described method embodiments when executing the computer-executable instructions in the memory. The processor can be a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processor (DSP), Microcontroller Unit (MCU), Neural Processing Unit (NPU), Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or other programmable logic devices. The aforementioned memory can be read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or solid-state drive, etc. The steps of the methods disclosed in the various embodiments of the present invention can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0113] Furthermore, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the various method embodiments of this application. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0114] Furthermore, embodiments of this application also provide a computer program product, including computer-executable instructions that, when executed by a processor, implement the steps in the above-described method embodiments.

[0115] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this application, if it refers to performing an action according to an element, it means performing the action at least according to that element, including two cases: performing the action only according to that element, and performing the action according to that element and other elements. Expressions such as "multiple," "repeatedly," and "various" include two, two times, two kinds, and more than two, more than two times, and more than two kinds.

[0116] The numbering used in describing the steps of a method does not inherently limit the order of these steps. For example, a step with a higher number does not necessarily have to be executed after a step with a lower number; it can be executed first and then second, or even in parallel, as long as this execution order is reasonable to someone skilled in the art. Similarly, multiple steps with consecutively numbered sequences (e.g., step 101, step 102, step 103, etc.) do not restrict other steps from being executed between them; for example, there can be other steps between step 101 and step 102.

[0117] This specification includes combinations of various embodiments described herein. Individual references to embodiments are made (e.g., "one embodiment," "some embodiments," or "preferred embodiments"); however, these embodiments are not mutually exclusive unless indicated to be mutually exclusive or are readily apparent to those skilled in the art. It should be noted that the word "or" is used in a non-exclusive sense throughout this specification unless the context explicitly indicates or requires it.

[0118] All references to this specification are considered to be incorporated integrally into the disclosure of this application so that they can serve as the basis for modifications if necessary. Furthermore, it should be understood that the above descriptions are merely preferred embodiments of this specification and are not intended to limit the scope of protection of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the scope of protection of one or more embodiments of this specification.

[0119] In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A multi-source, multi-objective coal conveying path optimization method for a user-friendly source-grid-load-storage system, characterized in that, Includes the following steps: Step S1: Static path optimization. Based on the directed graph model of the coal conveying network, for each coal conveying task to be executed, calculate the static optimal path with the minimum number of non-repeating nodes. Step S2: Dynamic scheduling and arrangement, obtain the static optimal path corresponding to all coal conveying tasks to be executed, and based on the nodes traversed by each optimal path in the directed graph, divide the tasks corresponding to paths without common nodes into the same execution batch for parallel execution, and divide the tasks corresponding to paths with common nodes into different execution batches for serial execution.

2. The method according to claim 1, characterized in that, The directed graph model of the coal conveying network is constructed in the following way: Each physical device unit in the coal conveying network is defined as a node in a directed graph; Each node is assigned a node type attribute, and the node type includes at least: coal yard node, belt conveyor node, merging node, and coal bunker node; Each node is assigned a quantitative attribute, which includes at least the node's maximum transport capacity and the node's current state; for coal yard nodes, the quantitative attribute also includes the preparation time cost for switching between different coal piles. The physical connection for unidirectional coal transport in a coal conveying network is defined as a directed edge in a directed graph.

3. The method according to claim 1, characterized in that, In step S1, the static optimal path is calculated for each coal conveying task to be executed, specifically including: For a task that requires taking coal from multiple source coal yards and transporting it to a target coal bunker, a depth-first search algorithm is used to search for all feasible paths from each source coal yard to the target coal bunker in the directed graph, and then combine them to obtain multiple candidate paths. Construct a cost function to evaluate the operating cost of each candidate path; The candidate path with the minimum cost function value is determined as the optimal path for the task.

4. The method according to claim 3, characterized in that, The cost function is the total number of non-repeating conveyor belt nodes occupied by all sub-paths in the candidate path, specifically expressed as: in, This refers to the total number of non-repeating conveyor belt nodes occupied by all sub-paths in the candidate path. The candidate path is the first Strip path The set of all belt conveyor nodes included. This represents the total number of all sub-paths included in the candidate path. This indicates merging all belt conveyor nodes in the candidate paths and removing duplicates. This indicates the number of elements in the set.

5. The method according to claim 3, characterized in that, After determining the optimal path, the process also includes a multi-source coal supply synchronization step, which specifically includes: Determine the first common intersection node of all sub-paths in the candidate path as the rendezvous point; Calculate the total time taken for each sub-path to reach the confluence point from the corresponding source coal yard. The total time is equal to the sum of the preparation time cost and transportation time of the source coal yard for that sub-path. The maximum value among the total times of all sub-paths is determined as the synchronization reference time; For each sub-path, calculate its delayed start time, which is equal to the synchronization reference time minus the total time consumed by the sub-path; Based on the aforementioned delayed startup time, startup instructions for each sub-path are generated.

6. The method according to claim 5, characterized in that, The transportation time is calculated in the following way: Transportation time = amount of coal to be transported in this sub-path / minimum transportation capacity of all nodes in this sub-path.

7. The method according to claim 1, characterized in that, In step S2, the task is divided into different execution batches, specifically including: Task priority sorting steps: Based on the planned coal transportation volume of each task, sort all coal transportation tasks to be executed in descending order to obtain the priority order of the coal transportation tasks to be executed. Batch scheduling steps: According to the priority order, the coal conveying tasks to be executed are divided into multiple execution batches in sequence; wherein, the node sets contained in the optimal paths corresponding to any two different tasks within the same execution batch are mutually exclusive.

8. The method according to claim 7, characterized in that, Also includes: The steps for calculating the total duration of the plan include: Calculate the total duration of a single task. The total duration of a task is equal to the maximum value of the preparation time cost of all source coal yards for that task plus the transportation time of that task. The transportation time is equal to the planned coal transport volume of that task divided by the minimum transportation capacity of all nodes on the optimal path of that task. Calculate the batch duration of an execution batch. The batch duration is equal to the maximum of the total task duration of all tasks within the batch. Calculate the total duration of the entire coal transportation plan. The total duration is equal to the sum of the durations of all executed batches.

9. The method according to claim 1, characterized in that, In step S1, a depth-first search algorithm is used; in step S2, a batch partitioning algorithm based on priority sorting and resource exclusivity constraints is used.

10. The method according to claim 1, characterized in that, The method is applied to the coal transportation scheduling of thermal power plants in a friendly source-grid-load-storage system.

Images