Method for realizing comprehensive raw material field belt real-time path optimization based on Dijkstra algorithm in combination with Fibonacci heap

Abstract

The invention discloses a method for realizing real-time path optimization of a comprehensive raw material field belt based on a Dijkstra algorithm in combination with a Fibonacci heap. The method comprises the following steps: S1) establishing a network model according to a raw material field belt; the method comprises the steps of (1) selecting a Fibonacci heap, (2) collecting data of related nodes to prepare a Fibonacci heap, (3) searching in the Fibonacci heap by using a Dijkstra algorithm, and (4) replanning a path in real time by using a search result, and can realize multi-node dynamic scheduling, dynamically planning a shortest transportation path to reduce no load or detour, and updating path calculation in real time to meet the real-time requirement.

Description

Technical Field

[0001] This invention belongs to the field of belt conveyor planning in the steel industry, specifically involving a method for real-time path optimization of belt conveyors in a comprehensive raw material yard based on Dijkstra's algorithm combined with Fibonacci stacks. Background Technology

[0002] As a crucial link in steel production logistics, the integrated raw material yard in the steel industry plays a vital role in connecting with the social logistics distribution chain and managing the internal logistics of the enterprise. Major users of raw materials and fuels include sintering, pelletizing, ironmaking, and lime kilns. The main tasks of the integrated raw material yard are to centrally receive, store, and manage raw materials and fuels, as well as to handle the transportation of raw materials and fuels between some user plants and the disposal of iron-containing waste from steel production. It serves as the logistics management center for raw materials and fuels in steel enterprises.

[0003] The raw material yard mainly includes receiving facilities, primary material yard facilities, mixing facilities, silo facilities, material supply and return facilities, sampling facilities, various auxiliary facilities, and the main control building. Internal transportation within the company uses belt conveyors, and the interfaces with each user are generally the receiving troughs or hoppers within each user's area.

[0004] The main facility used in the transportation route is the belt conveyor (or simply belt conveyor). The belt conveyor transports raw materials and fuels from the starting point on the supply side to the ending point on the demand side. The selected belt conveyor forms the transportation route.

[0005] Currently, the transportation routes in raw material yards mostly rely on manual intervention or fixed process control. However, with the large number of equipment, complex routes, and energy conservation and consumption reduction requirements in raw material yards, especially when new supply-side, demand-side, and belt conveyors are added, traditional methods are increasingly unable to meet the control needs of modern material yards. Summary of the Invention

[0006] To overcome the above-mentioned shortcomings, the purpose of this invention is to provide a method for real-time path optimization of conveyor belts in integrated raw material yards based on Dijkstra's algorithm combined with Fibonacci stack.

[0007] To achieve the above objectives, the present invention provides a method for real-time path optimization of conveyor belts in integrated raw material yards in the steel industry based on Dijkstra's algorithm combined with Fibonacci heaps, comprising the following steps: S1) Establish a network model based on the conveyor belt at the raw material yard; S2) Collect data from relevant nodes to prepare a Fibonacci heap; S3) Use Dijkstra's algorithm to search in the Fibonacci heap; S4) Utilize search results for real-time path replanning.

[0008] Furthermore, step S1: establishing a network model based on the raw material yard conveyor belt specifically involves: 21) Consider each starting point, ending point, and intersection of the belt conveyor in the raw material yard as a node in the diagram; 22) The edges connecting these nodes represent the belt conveyor lines, and each edge is assigned a weight value according to the actual situation.

[0009] Further, step S2, collecting data from relevant nodes to prepare a Fibonacci heap, specifically involves: 31) Collect the location information of all relevant nodes, the basic parameters of the conveyor belt, and its operating status; 32) Prepare a Fibonacci heap as a priority queue for Dijkstra's algorithm to efficiently manage the nodes to be explored.

[0010] Further, step S3, searching the Fibonacci heap using Dijkstra's algorithm, specifically involves: 41) Set the distance of the starting node to 0 and the distance of all other nodes to infinity; add all nodes to a Fibonacci heap and sort them by distance as the key. 42) Extract the node u with the smallest distance from the Fibonacci heap and mark it as visited; for each neighboring node v of node u, calculate the new cost to reach v through u; If the new cost is less than the cost of the current record, update the cost of v and adjust the position of the corresponding node in the Fibonacci heap; repeat the above process until the target node is found or all reachable nodes have been traversed.

[0011] Furthermore, step S4, performing real-time path replanning using search results, specifically involves: 51) When a fault is detected in a section of the belt, the network model is updated immediately, and the weight of the affected edge is set to infinity or the edge is removed directly. 52) Trigger Dijkstra's algorithm to find the shortest path from the current node to the target node again.

[0012] Furthermore, the method also includes: S5) Parameters are continuously optimized and adjusted based on actual operating conditions.

[0013] The present invention has the following advantages: Multi-node dynamic scheduling: For raw material yards with multiple stacking areas, loading and unloading points, transportation channels and other nodes, the system can dynamically plan the shortest transportation route to reduce empty loads or detours.

[0014] Real-time weight adjustment: The path weight can be dynamically adjusted based on the belt conveyor status (running / fault), energy consumption, etc., and the path calculation can be updated in real time.

[0015] Large-scale graph processing: The raw material yard logistics network may contain hundreds of nodes and thousands of edges, which can meet real-time requirements. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the method for real-time path optimization of conveyor belts in integrated raw material yards in the steel industry based on Dijkstra's algorithm combined with Fibonacci stacks, as described in this invention. Detailed Implementation

[0017] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] In the description of this invention, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] This invention is applied to the belt conveyor system of raw material yards in the steel industry. The optimization process of Dijkstra's algorithm combined with Fibonacci heap mainly revolves around the efficient operation of priority queues, thereby improving the real-time performance and dynamic response capability of path planning by reducing time complexity.

[0022] Dijkstra's algorithm, proposed by Dutch computer scientist Edsger W. Dijkstra in 1956, is a classic algorithm for finding the shortest path between two points in a graph. Its core idea is a greedy strategy, starting from the starting point and progressively expanding the shortest path tree outwards until all reachable vertices are covered. In each step, the algorithm selects the nearest unvisited vertex to the known shortest path tree and updates the shortest path estimate for its adjacent vertices.

[0023] The Fibonacci heap, proposed by Michael L. Fredman and Robert E. Tarjan in 1984, is a heap data structure that supports merge operations. It consists of a set of min-heap ordered trees, featuring merge and minimum element deletion operations with O(1) time complexity, and key reduction operations with O(1) amortized time complexity. These characteristics significantly improve the efficiency of implementing Dijkstra's algorithm with Fibonacci heaps.

[0024] Appendix Figure 1 As shown, the present invention provides a method for real-time path optimization of conveyor belts in integrated raw material yards based on Dijkstra's algorithm combined with Fibonacci stacks, comprising the following steps: Step S1: Network model establishment. This includes: Process P11. Node definition: Each starting point, ending point, and intersection of the belt conveyor is considered a node in the diagram.

[0025] Process P12. Edge definition and weight calculation: The edges connecting these nodes represent the belt conveyor lines. Each edge is assigned a weight value based on the actual situation. This value can be calculated based on factors such as distance, transportation time, and energy consumption.

[0026] S2. Data preparation and preprocessing. This includes: P21. Data Collection: Collect the location information of all relevant nodes, the basic parameters of the belt (length, width, maximum load capacity, etc.), and the operating status (normal, under maintenance, fault, etc.).

[0027] P22. Initialize a Fibonacci heap: Prepare a Fibonacci heap as a priority queue for Dijkstra's algorithm to efficiently manage the nodes to be explored.

[0028] S3. Dijkstra's algorithm execution. Includes: P31. Initialization Phase: Set the distance of the starting node to 0, and the distance of all other nodes to infinity. Add all nodes to a Fibonacci heap and sort them by distance as the key.

[0029] P32. Iterative Shortest Path Search: Extract the node u with the smallest distance from the Fibonacci heap and mark it as visited. For each adjacent node v of node u, calculate the new cost to reach v via u. If this new cost is less than the currently recorded cost, update the cost of v and adjust the position of the corresponding node in the Fibonacci heap. Repeat the above process until the target node is found or all reachable nodes have been traversed.

[0030] S4. Real-time route replanning. Includes: P41. Fault Detection and Handling: When a fault is detected in a section of the belt, the network model is updated immediately, and the weight of the affected edge is set to infinity or the edge is removed directly.

[0031] P42. Replanning the path: Triggers Dijkstra's algorithm to find the shortest path from the current node to the target node again.

[0032] S5. Continuous optimization. Parameters are continuously adjusted based on actual operating conditions, such as optimizing the operation of the Fibonacci heap and improving the weight calculation formula, to improve overall performance.

[0033] The present invention has been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the embodiments described above. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention. Many other changes and modifications made without departing from the concept and scope of the present invention should be considered within the scope of protection of the present invention.

[0034] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0035] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for realizing real-time path optimization of a comprehensive raw material field belt based on Dijkstra algorithm combined with Fibonacci heap, characterized in that, The method comprises the following steps: S1) establishing a network model according to the raw material field belt; S2) collecting data of relevant nodes to prepare a Fibonacci heap; S3) searching in the Fibonacci heap by using Dijkstra algorithm; S4) real-time path re-planning by using the search result.

2. The method for realizing real-time path optimization of a comprehensive raw material field belt based on Dijkstra algorithm combined with Fibonacci heap according to claim 1, characterized in that, The step S1: establishing a network model according to the raw material field belt specifically comprises: 21) regarding each starting point, ending point and intersection point of the raw material field belt as a node in the graph; 22) connecting the edges of the nodes to represent the belt line, and each edge is given a weight value according to the actual situation.

3. The method for realizing real-time path optimization of a comprehensive raw material field belt based on Dijkstra algorithm combined with Fibonacci heap according to claim 1, characterized in that, The step S2: collecting data of relevant nodes to prepare a Fibonacci heap specifically comprises: 31) collecting position information, basic parameters and running state of all relevant nodes; 32) preparing a Fibonacci heap as a priority queue for Dijkstra algorithm to efficiently manage the nodes to be explored.

4. The method for realizing real-time path optimization of a comprehensive raw material field belt based on Dijkstra algorithm combined with Fibonacci heap according to claim 1, characterized in that, The step S3: searching in the Fibonacci heap by using Dijkstra algorithm specifically comprises: 41) setting the distance of the starting node as 0, and the distance of all other nodes as infinite; adding all nodes to the Fibonacci heap and sorting them according to the distance as the key value; 42) extracting the node u with the minimum distance from the Fibonacci heap, and marking it as visited; for each adjacent node v of the node u, calculating the new cost of reaching v through u; if the new cost is less than the current recorded cost, updating the cost of v and adjusting the position of the corresponding node in the Fibonacci heap; repeating the above process until the target node is found or all reachable nodes are traversed.

5. The method for realizing real-time path optimization of a comprehensive raw material field belt based on Dijkstra algorithm combined with Fibonacci heap according to claim 1, characterized in that, The step S4: real-time path re-planning by using the search result specifically comprises: 51) when detecting a fault in a certain belt, immediately updating the network model, setting the weight of the affected edge as infinite or directly removing the edge; 52) triggering Dijkstra algorithm to find the shortest path from the current node to the target node again.

6. The method for realizing real-time path optimization of a comprehensive raw material field belt based on Dijkstra algorithm combined with Fibonacci heap according to claim 1, characterized in that, The method further comprises: S5) continuously optimizing and adjusting parameters according to the actual running situation.

Images