Molten iron transportation path planning method and system based on A* algorithm

Abstract

The invention discloses a molten iron transportation path planning method and system based on an A * algorithm, and relates to the technical field of train automatic path planning, and the method comprises the steps: obtaining track information, building a road network topological structure, and defining a conflict rule; optimizing the transportation path through an A * algorithm, and selecting a path planning strategy according to a conflict rule; and constructing a state transition diagram, and visually displaying a path planning result. According to the method, by establishing a road network topological structure, efficient abstraction and simplification of a railway network are achieved, a system can rapidly understand and process the train running state, a transportation path is optimized through an A * algorithm, optimization of the train running path is achieved, a path planning strategy is selected according to a conflict rule, and the train running state is optimized. According to the method, high efficiency of train operation can be realized on the premise of ensuring safety, direct presentation of a path planning result is realized by constructing the state transition diagram and performing visual display, and the method has better effects in the aspects of efficiency, safety and reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic train path planning, and in particular to a method and system for molten iron transportation path planning based on an A* algorithm. Background Art

[0002] In the field of steel production, efficient transportation of molten iron is a key link to improve production efficiency. With the development of industrial automation technology, path planning algorithms are increasingly used in molten iron transportation. Existing path planning technologies are mainly divided into two categories: blind search and heuristic search. Blind search algorithms, such as Dijkstra algorithm and Floyd algorithm, can ensure that the shortest path is found, but their global search characteristics lead to huge consumption of computing resources and low efficiency, making it difficult to adapt to industrial environments with high real-time requirements. In contrast, heuristic search algorithms, such as A* algorithm and ant colony algorithm, can improve search efficiency to a certain extent by introducing specific optimization rules, but they still have many limitations when facing complex and changeable industrial environments.

[0003] As a classic heuristic search algorithm, the core of the A* algorithm is to calculate the cost value of each node through a heuristic function to guide the search process. However, when processing large-scale map scenes, the A* algorithm is prone to generate a large number of redundant nodes, resulting in reduced search efficiency. In addition, when the environmental information changes, the A* algorithm does not adequately consider the node volume restrictions, making it difficult to achieve the expected results in practical applications. Although the existing weighted A* algorithm based on expanding the search neighborhood has improved the search efficiency to a certain extent, it has high requirements on device performance and needs to rely on sensors to collect point cloud information during the environmental modeling process, which increases the difficulty of implementation.

[0004] In view of the shortcomings of the existing technology, technical personnel in this field have been seeking more efficient and practical path planning methods. The particularity of the railway structure of the steel yard requires that the path planning method should not only consider the connection relationship between nodes, but also take into account the geometric position and attributes of the space. At this point, the combination of topological network and grid structure provides a new idea for path planning. However, how to avoid repeated calculations and optimize the routing process while maintaining search efficiency is still a challenge facing current technology. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: the existing path planning method has low search efficiency, huge consumption of computing resources, and difficulty in adapting to real-time requirements, as well as the problem of how to avoid repeated calculations and optimize the routing process while maintaining search efficiency.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a method for molten iron transportation path planning based on the A* algorithm, comprising obtaining track information, establishing a road network topology, and defining conflict rules; optimizing the transportation path through the A* algorithm, and selecting a path planning strategy according to the conflict rules; constructing a state transition diagram to visualize the path planning results.

[0008] As a preferred solution of the molten iron transportation path planning method based on the A* algorithm described in the present invention, the establishment of the road network topology structure includes establishing a map.json file to enter track information, and reading the map.json file to load and display train status data.

[0009] As a preferred solution of the molten iron transportation path planning method based on the A* algorithm described in the present invention, the conflict rules defined include hard constraints and soft constraints, and the conflict rules are defined by determining the train occupation section.

[0010] As a preferred solution of the molten iron transportation path planning method based on the A* algorithm described in the present invention, wherein: the optimization of the transportation path by the A* algorithm includes establishing a priority queue, adding the initial state of the train to the priority queue, and constructing a node cost function, which is expressed as:

[0011] f(x)=g(x)+h(x)

[0012] Where x is the train, f(x) is the estimated cost of the train transportation path, g(x) is the actual cost of the train from the starting point to the current node, and h(x) is the cost of the train from the current node to the target node. A queue is established to record the predecessor nodes, and the calculated g and h values ​​of each predecessor node are stored. The node with the smallest f value is returned from the priority queue as the current state. Based on the current state, the length of the track in the next state is simulated as the estimated cost to the destination.

[0013] As a preferred solution of the molten iron transportation path planning method based on the A* algorithm described in the present invention, wherein: the path planning strategy selected according to the conflict rule includes checking whether there is a conflict between the current state and other train states, and recording the number of conflicts; the path planning strategy includes a stop and wait strategy and a detour strategy. If the stop and wait cost is smaller than the detour cost, the g value and h value of the current state are updated, and the g value and h value are added to the priority queue; if the detour cost is small, jump to the next node without conflict.

[0014] As a preferred solution of the molten iron transportation path planning method based on the A* algorithm described in the present invention, wherein: the construction of the state transition diagram includes constructing a state transition diagram according to the initial state and target state of the train scheduling problem, and performing path planning through the A* algorithm.

[0015] As a preferred solution of the molten iron transportation path planning method based on the A* algorithm described in the present invention, the visual display includes using an svg file to display the train trajectory and signal light status.

[0016] Another object of the present invention is to provide a molten iron transportation path planning system based on the A* algorithm, which can optimize the transportation path through the A* algorithm and select the path planning strategy according to the conflict rules, thereby solving the problem of low search efficiency in the current path planning method.

[0017] As a preferred solution of the molten iron transportation path planning system based on the A* algorithm described in the present invention, it includes: a track information and road network management module, a path optimization and conflict processing module, and a state transfer and visualization display module; the track information and road network management module is used to obtain track information, establish a road network topology structure, and define conflict rules; the path optimization and conflict processing module is used to optimize the transportation path through the A* algorithm and select the path planning strategy according to the conflict rules; the state transfer and visualization display module is used to construct a state transfer diagram and visualize the path planning results.

[0018] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a molten iron transportation path planning method based on an A* algorithm.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for planning a molten iron transportation path based on an A* algorithm.

[0020] Beneficial effects of the present invention: The molten iron transportation path planning method based on the A* algorithm provided by the present invention achieves efficient abstraction and simplification of the railway network by acquiring track information and establishing a road network topology structure, so that the system can quickly understand and process the train operation status, optimize the transportation path through the A* algorithm, and achieve the optimization of the train operation path. The path planning strategy is selected according to the conflict rules, which can achieve high efficiency of train operation while ensuring safety. By constructing a state transition diagram and visually displaying it, the intuitive presentation of the path planning results is achieved. The present invention achieves better results in terms of efficiency, safety and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0022] Figure 1 An overall flow chart of a molten iron transportation path planning method based on the A* algorithm provided for the first embodiment of the present invention.

[0023] Figure 2 A technical roadmap for a molten iron transportation path planning method based on the A* algorithm is provided for the first embodiment of the present invention.

[0024] Figure 3 A position state diagram of four trains in the initial state of a molten iron transportation path planning method based on an A* algorithm provided in the second embodiment of the present invention.

[0025] Figure 4 A position state diagram of four trains in the first step of an A* search of a molten iron transportation path planning method based on an A* algorithm provided in a second embodiment of the present invention.

[0026] Figure 5 A position state diagram of four trains in the last step of an A* search of a molten iron transportation path planning method based on an A* algorithm provided in the second embodiment of the present invention.

[0027] Figure 6 A schematic diagram of a module of a molten iron transportation path planning system based on an A* algorithm provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0029] Example 1, reference Figure 1-Figure 2 , as an embodiment of the present invention, provides a method for planning a molten iron transportation path based on an A* algorithm, comprising:

[0030] S1: Obtain track information, establish road network topology, and define conflict rules.

[0031] Furthermore, establishing the network topology structure includes creating a map.json file to enter track information, and reading the map.json file to load and display train status data.

[0032] It should be noted that the definition of conflict rules includes hard constraints and soft constraints. The conflict rules are defined by determining the train occupation section. When the train completely enters the track, it is determined whether the next track is a red light band; if it is a red light band or a white light band, there is a hard constraint conflict and the path planning strategy is executed; based on whether it is a blue light band, it is determined whether the next track is occupied by other trains. If it is occupied, there is a hard constraint conflict and the path planning strategy is executed; if the next track is not occupied by other trains, the output is no conflict and the journey continues; when the train has not completely entered the track, the output is no conflict and the journey continues; when there is no hard constraint conflict, it is determined whether there is a soft constraint conflict; if there is a soft constraint conflict, the path planning strategy is evaluated and the optimal path is executed; if there is no soft constraint conflict, the output is no conflict and the journey continues.

[0033] It should also be noted that the network topology is established, the railway network is regarded as a topological structure, and the connectivity between elements is concerned, rather than the specific location of the elements. During operation, only the positioning device on the train needs to send back GPS data to the routing system to determine which track the train is on or occupied at this time. In the railway network, each track is connected by an insulating joint and a switch. Next to the insulating joint and the switch are set up signal lights that turn on in the left and right directions to indicate the direction and whether there are other trains passing in front. The map.json file is established, and the actual track length, adjacent signal lights, switch reverse status and insulating joint relationship light data are entered. The network structure is initialized, and the map.json file is read. The data of each part, including the actual track length, adjacent signal lights, switch reverse status and insulating joint relationship, are taken out and stored in dictionaries with different names in turn. The train status states is represented in the form of nested tuples as follows:

[0034] states

[0035] =((PercentageOfDriven,TrainLength,Speed,Significance),(['SignalLight1']),(['SignalLight2']))

[0036] Among them, PercentageOfDriven is the percentage of mileage traveled (a decimal between 0 and 1), TrainLength is the train length, Speed ​​is the train speed, Significance is the task priority, SignalLight1 and SignalLight2 are the names of the signal lights at both ends of the track where the starting point is located; if there are multiple trains, multiple tuples can be nested in states.

[0037] It should also be noted that if Figure 2As shown, the conflict rule is set to detect whether there is a route conflict between vehicles running at the same time. The conflict rule definition also includes first determining the train occupied section, turning on the red lights at both ends of the track occupied by the train, and turning on the white light of the next track to be entered. The cumulative travel distance is calculated by the remaining mileage percentage to determine whether the train is completely inside a track or at the junction of two tracks. Based on this, the occupied section occupied_rails is returned. The occupied section is the intersection of the red light band and the white light band. Next, the subsequent running status of the train is discussed, and the remaining time RemainTime of the train to the next track is calculated, which is expressed as:

[0038] RemainTime=(1-PercentageOfDriven)*RailLength / Speed

[0039] Among them, PercentageOfDriven is the percentage of the train that has been running on the current track, and RailLength is the track length. If there is a running train, that is, RemainTime is not empty, check the adjacent tracks of the occupied section, obtain the possible routes and signal light switch conditions of all waiting trains, and detect whether there is a conflict. If so, adopt a path planning strategy based on the constraints.

[0040] The hard constraints include that when multiple trains are running at the same time, the occupied sections of each train should not intersect in principle. The get_occupied_circuits function is established to calculate and return the set of tracks occupied by a given train state. The get_occupied_circuits function is a key component in the train scheduling problem. It can determine which track sections a train will occupy under a specific state. The input parameter state of the function is a tuple containing three elements:

[0041] state[0]: A tuple containing two elements, the first element indicates the total length of the train, and the second element indicates the current position of the train.

[0042] state[1]: A list of track segment identifiers that the train has passed through, representing the train's historical path.

[0043] state[2]: A list containing future track segment identifiers, representing the expected path of the train.

[0044] The function first calculates the train's red light band occupancy based on the total length of the train and the current position. The red light band refers to the track segment occupied by the train during its travel, including the front and rear of the train. During the calculation process, a dictionary named Dlen2 is used to calculate the length of each track segment based on the track segment identifier. The train's red light band occupancy is determined by iterating the track segment identifiers in state[1] and adding the track segment identifiers to the corresponding lengths in the Dlen2 dictionary. Next, the function calculates the train's white light band occupancy. The white light band refers to the track segment in front of the train, usually a distance in front of the train, and only considers the future track segments in state[2]. Finally, the function determines the total track set occupied_rails occupied by the train by calculating the union of the red light band and the white light band, and uses it as the function's return value.

[0045] The soft constraints include that if the recommended routes returned by multiple trains are in the same section, multiple trains pass through in the order of task priority.

[0046] S2: Optimize the transportation path through the A* algorithm and select the path planning strategy according to the conflict rules.

[0047] Furthermore, the optimization of the transportation path by the A* algorithm includes establishing a priority queue, adding the initial state of the train to the priority queue, and constructing a node cost function, which is expressed as:

[0048] f(x)=g(x)+h(x)

[0049] Where x is the train, f(x) is the estimated cost of the train transportation path, g(x) is the actual cost of the train from the starting point to the current node, and h(x) is the cost of the train from the current node to the target node. A queue is established to record the predecessor nodes, and the calculated g and h values ​​of each predecessor node are stored. The node with the smallest f value is returned from the priority queue as the current state. Based on the current state, the length of the track in the next state is simulated as the estimated cost to the destination.

[0050] It should be noted that selecting the path planning strategy according to the conflict rules includes checking whether there is a conflict between the current state and other train states, and recording the number of conflicts; the path planning strategy includes a stop-and-wait strategy and a detour strategy. If the cost of stop-and-wait is smaller than that of detour, the g value and h value of the current state are updated, and the g value and h value are added to the priority queue; if the cost of detour is small, jump to the next node without conflict.

[0051] It should also be noted that the A* algorithm uses an open list and a closed list to maintain nodes in the search process. The open list contains nodes to be evaluated, and the closed list contains nodes that have been evaluated. The algorithm starts from the initial node, adds the initial node to the open list, and sets the estimated cost f of the train transportation path. In each iteration, the algorithm selects the node with the smallest f value in the open list for expansion, and adds the adjacent nodes of the node with the smallest f value to the open list. If the node has been added to the closed list, it will no longer be expanded. The f value is f_score. The smaller the f_score, the better the path from the starting point to the target. The g value is g_score, which is determined by the actual track length and is equal to the sum of the lengths of the tracks passed by the train. The h value is h_score, which is the length of the track that may be entered in the next state of the simulation. It is updated as the f value accumulates. The A* algorithm process is as follows:

[0052] Step 1: Create a priority queue priority_queue to store the initial state of the train start_state. Each element is a tuple (f_score, start_state). The initial cost function value from the starting point to this point is set to 0.

[0053] Step 2: Establish a queue came_from to record the predecessor nodes. In order to improve the efficiency and adaptability of the A* algorithm, a memoization search method is adopted: the calculated g and h values ​​of each predecessor node are stored in came_from to avoid repeated calculations.

[0054] Step 3 returns a minimum value from priority_queue as the current state current_state. If the target node goal_state has been reached, add the target node and return to the predecessor node list in reverse order, introduce the neighbor node neighbor, traverse the nodes in the next state next_states and assign them to neighbor.

[0055] Step 4 checks whether the status of neighbor conflicts with other trains running at the same time. The number of conflicts is n. Avoid neighbor first, search for (4-n) possible child nodes in the next state, and calculate the remaining mileage as the estimated cost h_score to the destination. h_score is updated every time a decision point / node is passed.

[0056] Step 5: Selection of detour strategy and stop-and-wait strategy. Introduce the inter-layer cost layer_cost. This parameter can be set to an appropriate value or interval according to the actual situation. Calculate the f_score from the starting point to a neighbor node. The tentative_g_score is the preliminary g value, which indicates the shortest cost from the starting point to the neighbor, expressed as:

[0057] tentative_g=g current_state +layer_cost

[0058] If the neighbor neighbor is not in g_score or the initial g value is less than g neighbor , indicating that the relative cost of waiting is lower than that of taking a detour, so the waiting strategy should be selected. Therefore, the shortest cost from the starting point to the neighbor is updated, and the initial g value is assigned to g neighbor , expressed as:

[0059] g neighbor =tentative_g

[0060] Calculate f_score, add the neighbor to the priority queue priority_queue, and record the predecessor node came_from, expressed as:

[0061] f = g neighbor +h

[0062] came_from neighbor =current_state

[0063] Otherwise, a detour strategy is adopted, tentative_g is abandoned, and the next node without conflict is jumped to.

[0064] Step 6: Backtrack to came_from and return the optimal path solution.

[0065] It should also be noted that the design of the cost function is closely related to the specific scenario and requirements of the problem. In some cases, it may be necessary to choose between a detour strategy and a stop-and-wait strategy. The detour strategy refers to finding a path that avoids obstacles or high-cost areas, even if this may result in a longer total path. The stop-and-wait strategy is to choose to wait in a safe or low-cost location when the current path is blocked until you can continue to move forward.

[0066] S3: Construct a state transition diagram to visualize the path planning results.

[0067] Furthermore, constructing a state transition diagram includes constructing a state transition diagram according to an initial state and a target state of the train scheduling problem, and performing path planning through an A* algorithm.

[0068] It should be noted that the visual display includes the use of svg files to display the train track and signal light status.

[0069] It should also be noted that the path planning method of the present invention can plan paths for multiple trains at the same time, fully consider the mutual influence between trains, and effectively avoid potential conflicts. In addition, it can intelligently plan the optimal routes for picking up and placing tanks according to the specific transportation tasks of the trains, thereby improving transportation efficiency. It is worth mentioning that the path planning method of the present invention can also flexibly adjust the path planning scheme according to the real-time status of the train and the dynamic changes of the transportation network, so as to ensure that the efficiency and safety of transportation are always maintained in a complex and changeable industrial environment.

[0070] It should also be noted that the status of the signal light includes: a blue light strip indicates unlocked, a white light strip indicates the next node simulated by the A* algorithm, and a white light indicates the optimal path recommended by the A* algorithm.

[0071] Example 2, reference Figure 3-Figure 5 , which is an embodiment of the present invention, provides a molten iron transportation path planning method based on the A* algorithm. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0072] The first step is to build a state transition graph and create a function called find_leaves. The function accepts a dictionary tree representing the state transition graph as input. In this dictionary, the key represents the child node and the value represents the parent node. The function finds all leaf nodes, that is, states without child nodes, by calculating the difference between the child node set and the parent node set.

[0073] Next, define a function called create_pruned_graph_optimized to create a pruned directed graph G_pruned based on the tree dictionary and the leaf node set. In this process, only states that are not leaf nodes will be added to the graph, which can exclude states that will not appear in the optimal path, thereby optimizing the search process.

[0074] In the second step, the A* algorithm is applied. The initial_state and goal_state are sets of two tuples, representing the initial state and goal state of the train scheduling problem, respectively. Each tuple contains three elements: the position of the train, a list of possible operations, and a list of the next state. The get_route function accepts an index list sel as input, extracts the corresponding state from the initial_state and goal_state according to this list, and uses the A* algorithm to accept the initial state, target state, and layer_cost as input, and returns the optimal path. The get_occupied_circuits function is used to calculate the track resources occupied on a given path. The get_route function collects the track resources occupied on all paths by calling this function and returns a collection containing all occupied resources.

[0075] Next, create the group_trains function to implement train grouping. It initializes each train as a separate group and calculates the track resources occupied by each group through an iterative process. If the intersection of track resources is found between different groups, these groups will be merged. This process continues until there are no more merging possibilities, and finally returns an optimal grouping result.

[0076] The third step is to visualize the path plan. Four trains at different positions are randomly selected, marked as car 1, car 2, car 3 and car 4, to undertake different molten iron transportation tasks. Car 1 and car 2 go to blast furnace I to pick up tanks and send them to the inspection station where car 3 and car 4 are located; car 3 and car 4 go to blast furnace II to pick up tanks and send them to the inspection station where car 1 and car 2 are located. The experimental dispatcher sets the optimal route that complies with railway regulations for the four trains, compares it with the optimal route returned by the recommendation system, and uses svg files to display the driving trajectories. The trajectories of the four trains are filled with four colors: red, orange, yellow, and purple. For signal lights, a blue light strip indicates unlocked (idle), a colored line segment indicates that the train is traveling or occupying this section, a white light strip indicates the next node simulated by the A* algorithm (including neighbor), and a white light indicates the signal light that needs to be turned on as recommended by the A* algorithm.

[0077] pass Figure 3-Figure 5 It can be seen that during the experiment, the four trains successfully achieved the transportation goal while preventing mutual interference, and the path recommended by the system was highly consistent with the manually predicted path. The vehicle driving trajectory was visualized in SVG format, and the sections with exploration potential were clearly identified. The entire railway network layout was displayed on a two-dimensional plane, allowing users to quickly identify and decide on the best railway route when using the system for path planning. Therefore, our invention is creative.

[0078] Example 3, reference Figure 6 , which is an embodiment of the present invention, provides a molten iron transportation path planning system based on the A* algorithm, including a track information and road network management module, a path optimization and conflict handling module, and a state transfer and visualization display module.

[0079] The track information and road network management module is used to obtain track information, establish road network topology, and define conflict rules; the path optimization and conflict handling module is used to optimize the transportation path through the A* algorithm and select the path planning strategy according to the conflict rules; the state transition and visualization display module is used to construct the state transition diagram and visualize the path planning results.

[0080] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0081] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0082] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0083] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0084] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for planning a molten iron transportation path based on an A* algorithm, characterized in that: include: Obtain track information, establish road network topology, and define conflict rules; The transportation route is optimized through the A* algorithm, and the path planning strategy is selected according to the conflict rules; Construct a state transition diagram to visualize the path planning results.

2. The molten iron transportation path planning method based on the A* algorithm as claimed in claim 1, characterized in that: The establishment of the road network topology structure includes establishing a map.json file to enter track information, and reading the map.json file to load and display train status data.

3. The molten iron transportation path planning method based on the A* algorithm as claimed in claim 2, characterized in that: The conflict rules defined include hard constraints and soft constraints, and the conflict rules are defined by determining the train occupied section.

4. The molten iron transportation path planning method based on the A* algorithm as claimed in claim 3 is characterized in that: The optimization of the transport path by the A* algorithm includes establishing a priority queue, adding the initial state of the train to the priority queue, and constructing a node cost function, which is expressed as: f(x)=g(x)+h(x) Where x is the train, f(x) is the estimated cost of the train transportation path, g(x) is the actual cost of the train from the starting point to the current node, and h(x) is the cost of the train from the current node to the target node; Establish a queue to record the predecessor nodes, store the calculated g and h values ​​of each predecessor node, return the node with the smallest f value from the priority queue as the current state, and simulate the length of the track in the next state based on the current state as the estimated cost to the end point.

5. The molten iron transportation path planning method based on the A* algorithm as claimed in claim 4, characterized in that: The selecting of the path planning strategy according to the conflict rule includes checking whether the current state conflicts with other train states and recording the number of conflicts; The path planning strategy includes the stop-and-wait strategy and the detour strategy. If the stop-and-wait strategy is less costly than the detour strategy, the g value and h value of the current state are updated and added to the priority queue. If the detour cost is small, jump to the next node without conflict.

6. The molten iron transportation path planning method based on the A* algorithm as claimed in claim 5, characterized in that: The state transition diagram is constructed according to the initial state and target state of the train scheduling problem, and the path planning is performed by using the A* algorithm.

7. The method for planning a molten iron transportation path based on the A* algorithm according to claim 6, characterized in that: The visual display includes using an svg file to display the train track and signal light status.

8. A system using the molten iron transportation path planning method based on the A* algorithm as claimed in any one of claims 1 to 7, characterized in that: It includes track information and road network management module, path optimization and conflict handling module, state transfer and visualization display module; The track information and road network management module is used to obtain track information, establish road network topology, and define conflict rules; The path optimization and conflict handling module is used to optimize the transportation path through the A* algorithm and select the path planning strategy according to the conflict rules; The state transfer and visualization module is used to construct a state transfer diagram and visualize the path planning results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the molten iron transportation path planning method based on the A* algorithm described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the molten iron transportation path planning method based on the A* algorithm described in any one of claims 1 to 7 are implemented.

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