CN111340296A - Honeycomb labyrinth shortest path calculation method and honeycomb labyrinth practical training system - Google Patents
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Abstract
The invention discloses a honeycomb maze shortest path calculation method and a honeycomb maze practical training system, wherein each node of a honeycomb maze is used as a unit structure body, all nodes adjacent to each node are numbered, and pointers of the adjacent nodes are stored in the unit structure body of the node; reading data of nodes and edges, storing the data in an adjacent matrix form, and counting the number of the nodes and the number of the edges; in the process of the adjacent matrix operation, all the other connection points are set as maximum values after each node traverses the adjacent node; acquiring node information of a starting node, a destination node and each intermediate node; the shortest distance from the starting point to the end point through these intermediate nodes is calculated by dijkstra's algorithm. The invention solves the problem that the honeycomb labyrinth shortest path algorithm cannot be realized in a low-configuration singlechip, adopts an efficient algorithm in a low-cost hardware environment, can be used for various education training products, and obviously reduces the product cost.
Description
Technical Field
The invention relates to the technical field of education and practical training, in particular to a honeycomb labyrinth shortest path calculation method and a honeycomb labyrinth practical training system.
Background
The programming education is a course for training the computational thinking and the innovation and difficulty-relieving ability of students through courses such as programming game enlightenment, visual graphic programming and the like. The shortest path calculation is to enable students to search the shortest path between two points on the existing map, and is a classic course for inspiring the logical thinking ability of the students. The shortest path algorithm is very common in solving the application related to path search, including network routing algorithm, robot path finding, artificial intelligence, game design and the like, and is more widely applied in the fields of traffic route navigation and path analysis. The common shortest path search algorithm includes Dijkstra (Dijkstra algorithm) and a-Star algorithm, but the RAM of the current mainstream single-chip microcomputer is below 64K, and the shortest path search algorithm is difficult to operate.
Disclosure of Invention
The invention provides a honeycomb labyrinth shortest path calculation method and a honeycomb labyrinth practical training system which can be applied to a low hardware configuration environment.
In order to solve the technical problems, the technical scheme of the invention is as follows:
on one hand, the invention provides a honeycomb labyrinth shortest path calculation method, which comprises the following steps:
taking each node of the honeycomb maze as a unit structure body, wherein the attributes of the unit structure body comprise unit coordinates, pointers of adjacent nodes, unit node weight and the number of the adjacent nodes; numbering all nodes adjacent to each node, and storing pointers of the adjacent nodes into a unit structure body of the node;
reading data of nodes and edges, storing the data in an adjacent matrix form, and counting the number of the nodes and the number of the edges; the elements in the adjacent matrix are n and max, wherein n represents the weight value directly connected with edges, and max represents that no edges are directly connected;
in the operation process of the adjacent matrix, after each node traverses the adjacent node, the other connection points are all set as max;
and acquiring node information of the starting node, the end node and each intermediate node, and calculating the shortest distance from the starting point to the end point of each intermediate node by using a Dijkstra algorithm.
Preferably, the method further comprises the steps of: nodes that the target path does not pass through are deleted from the network graph.
Preferably, the step of deleting the node which the target path does not pass through from the network graph is as follows: and starting from the starting point, deeply traversing the network graph, deleting nodes which are not traversed from the original network graph, and generating a new network graph to replace the original network graph.
Preferably, before storing the pointer of each neighboring node into the unit data group, the method further includes:
and when the unit coordinate position of a node is out of the area range of the network graph, deleting the node.
On the other hand, the invention also provides a honeycomb labyrinth practical training system, which comprises:
the honeycomb unit is provided with a button for setting a starting point and an end point, and six electric bridges which are respectively communicated with six sides and used for sensing whether adjacent nodes are connected or not, wherein the six electric bridges are communicated with each other;
a master device communicatively coupled to each of the cells, the master device being capable of performing the cell maze shortest path computation method as described above.
Preferably, each of the honeycomb units is provided with a signal lamp controlled by the main control device.
Preferably, the master control device comprises a control chip of the STM32 series.
By adopting the technical scheme, the required map is arranged by the plurality of honeycomb units, and the control of each honeycomb unit is realized by the main control equipment. And performing map construction, adjacent matrix optimization, shortest distance calculation and the like on the main control equipment. The algorithm can effectively execute the shortest path algorithm passing through the designated node on low-configuration equipment, particularly low-RAM equipment and a single chip microcomputer. By adopting the practical training system, the terrain can be constructed at will, the failure rate is reduced, and the interest of students in science and technology is stimulated by exploring and finding the students in games.
Drawings
FIG. 1 is a flowchart illustrating steps of a method for calculating shortest path in honeycomb maze according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating neighbor node numbering of a node according to the present invention;
FIG. 3 is a schematic diagram of a neighbor matrix according to the present invention;
FIG. 4 is a schematic diagram of a honeycomb unit structure of the honeycomb labyrinth training system of the present invention;
fig. 5 is a schematic view of a labyrinth structure of the honeycomb labyrinth training system of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In a first aspect, referring to fig. 1, the present invention provides a method for calculating a shortest path in a honeycomb maze, comprising the steps of:
s10: taking each node of the honeycomb maze as a unit structure body, wherein the attributes of the unit structure body comprise unit coordinates, pointers of adjacent nodes, unit node weight and the number of the adjacent nodes; numbering all nodes adjacent to each node, and storing pointers of the adjacent nodes into a unit structure body of the node;
s20: reading data of nodes and edges, storing the data in an adjacent matrix form, and counting the number of the nodes and the number of the edges; the elements in the adjacent matrix are n and max, wherein n represents the weight value directly connected with edges, and max represents the number directly connected without edges, and max is a number which is defined in advance and is large enough;
s30: in the process of the adjacent matrix operation, after each node traverses the adjacent node, the other connection points are all set as max;
s40: acquiring node information of a starting node, a destination node and each intermediate node; the shortest distance from the starting point to the end point through these intermediate nodes is calculated by dijkstra's algorithm.
Specifically, the method further comprises the following steps: nodes that the target path does not pass through are deleted from the network graph. In addition, nodes which the target path passes through need to be deleted from the network graph, and repeated calculation is avoided.
Specifically, the step of deleting the nodes that the target path does not pass through from the network graph includes: and starting from the starting point, deeply traversing the network graph, deleting nodes which are not traversed from the original network graph, and generating a new network graph to replace the original network graph.
Specifically, before storing the pointer of each neighboring node into the unit data group, the method further includes: and when the unit coordinate position of a node is out of the area range of the network graph, deleting the node.
In a second aspect, the present invention further provides a honeycomb labyrinth training system, including:
the honeycomb unit is provided with a button for configuring a starting point and an end point, and six electric bridges which are respectively communicated with six sides and used for sensing whether adjacent nodes are connected or not, wherein the six electric bridges are communicated with each other; in use, the honeycomb unit is set to a starting point or an end point by pressing a button.
The main control equipment is in communication connection with each cell unit, and can execute the cell maze shortest path calculation method.
Specifically, each honeycomb unit is provided with a signal lamp controlled by the main control equipment. The starting point and the terminal point of the user are visually prompted through the signal lamp, and the training teaching device is convenient to use in the training teaching.
Specifically, the master control device includes a control chip of the STM32 series. It should be noted that, in the present invention, other control chips meeting the performance requirement may also be used to implement the function of the main control device.
By adopting the technical scheme, the required map is formed by arranging the honeycomb units, and the control of each honeycomb unit is realized through the main control equipment. The method comprises the steps of map construction, adjacent matrix optimization, shortest distance calculation and the like in the main control equipment. The algorithm can effectively execute the shortest path algorithm passing through the designated node on low-configuration equipment, particularly low-RAM equipment and a single chip microcomputer. By adopting the practical training system, the terrain can be constructed at will, the failure rate is reduced, and the interest of students in science and technology is stimulated by exploring and finding the students in games.
Referring to fig. 5, in order to solve the above technical problem, the following technical solutions are proposed in another embodiment of the present invention:
and (5) constructing a map. The maze structure is initialized, and the honeycomb maze is mainly characterized in that each node has six adjacent nodes, so the density of the path is greater than that of the maze taking a quadrangle as a unit, not only the calculation of the path but also a moving algorithm is considered, and a comprehensive node class is required to be constructed to facilitate the map layout and the subsequent calculation. Firstly, a class CELL is constructed for initializing a maze unit node. The main attributes of CELL are:
the object position x, y is used to store the coordinates of the unit node, which is used for the layout of the map.
The array neighbor [ ] stores pointers to neighbor nodes.
The variable quanzhi _ is used to store the weight of the unit node.
In order to calculate the adjacent nodes, a one-dimensional array cellList [ n × m ] with the length of n × m is adopted in the algorithm to store the maze nodes.
And constructing adjacent nodes, and numbering the adjacent nodes of each node as shown in fig. 2. As shown in fig. 5, in the process of constructing the map, the left border node may lack the adjacent node No. 2, and the right border node may lack the adjacent node No. 3. The current node position is CELL position { x, y }, even row 0 neighbor position { x, y-1}, 1 neighbor position { x +1, y-1}, 2 neighbor position { x +1, y }, 3 neighbor position { x +1, y }, 4 neighbor position { x, y +1}, 5 neighbor position { x-1, y }, 0 neighbor position { x-1, y-1} when odd row, 1 neighbor position { x, y-1}, 2 neighbor position { x +1, y }, 3 neighbor position { x, y +1}, 4 neighbor position { x-1, y +1}, 5 neighbor position { x-1, y }. When the position satisfies if (| (x <0| | y <0| | | x > cols-1| | | y > rows-1)), the node pointer of the corresponding position is placed in the neighbor.
And extracting data of the points and the edges, reading the data of the nodes and the edges from the file, storing the data in an adjacent matrix form, and counting the number of the nodes and the number of the edges. As shown in fig. 3, the elements in the adjacency matrix are only n and max, where n represents the weight value directly connected by edges, and max represents the weight value directly connected by no edges. max is a predefined sufficiently large number.
And deleting nodes which the target path does not pass through from the original network graph, and pruning. The specific method is that, starting from the starting point, the graph is traversed with depth first, and nodes which are not traversed can not appear on the target path definitely, so that the nodes can be cut off from the original network graph. Thereby generating a new network map to replace the original network map. The purpose of pruning is to greatly improve the space-time efficiency of the algorithm by reducing the number of nodes in the network under the condition of not influencing the objective path calculation.
Optimizing the adjacency matrix, because each CELL has 6 adjacent nodes at most, the adjacency matrix has a large number of max values and occupies a large space when the map is enlarged. Therefore, in the process of the adjacency matrix operation, after each node traverses the own adjacent node, the rest of the connection points are max.
The shortest path algorithm used in the embodiment of the invention is as follows:
(1) assuming that the number of designated intermediate nodes needing to pass through is n, judging the communication condition between the n nodes, if any two nodes are not communicated, the path meeting the condition does not exist; otherwise, the next step is carried out. The method for judging whether the communication is performed comprises the following steps: one of the n nodes is selected as a root node, the depth is firstly traversed through the graph, and if all other n-1 nodes are traversed, the nodes are connected; otherwise, it is not connected.
(2) And fully arranging the n intermediate nodes to generate an intermediate node sequence. The starting point of the full alignment is denoted as V1 and the end point is denoted as Vn. The shortest distance and path between V1 and Vn are calculated. The specific method is the method for solving the distance and the path between the intermediate nodes.
(3) And (4) calculating the local shortest path from the target source point to V1, namely the shortest path of the single source point, and directly using Dijkstra algorithm. If the path between the target source point and V1 passes through the free nodes, the passed free nodes are saved in order into the local path.
(4) The local shortest path from V1 to Vn through all intermediate nodes is found in (2).
(5) And (4) solving a local shortest path from the Vn to a target end point, namely the shortest path of the single source point, and directly using Dijkstra algorithm. And if the path between the Vn and the target end point passes through the free nodes, saving the passed free nodes into the local path in sequence.
(6) Connecting the above 3 paths in sequence to obtain the final productmidThe (to be selected) global shortest path of all nodes in the cluster. And adding the distances of the 3 paths to obtain the distance of the shortest path of the whole process to be selected.
(7) And (4) searching the path obtained in the step (6), wherein the path with the minimum distance is the shortest distance in the whole process to be obtained, and the corresponding path is the shortest path to be obtained.
The honeycomb labyrinth training system in the embodiment of the invention comprises a honeycomb unit and a main control device. The honeycomb units are used for constructing a honeycomb labyrinth structure, the field information is transmitted to the main control equipment through electric signals, the main control equipment calls an algorithm preinstalled on the equipment to solve the shortest path and the path nodes and returns the result to each honeycomb unit, and if the nodes on the shortest path are lightened.
A cellular unit, a practical training system, uses the unit shown in fig. 4, wherein the middle button is used to configure the start point and the end point, and its inner 6 power rails are used to sense whether the adjacent nodes are connected or not. This has the advantage that all cells can be configured and moved at will, already meeting different terrain designs.
The main control device, in this embodiment, uses an STM32F4 series high performance microcontroller of ST, which uses a 90 nm NVM process and ART (Adaptive Real-Time memory accelerator). The STM32F4 series can reach 210DMIPS @168 MHz. The adaptive real-time accelerator can completely release the performance of a Cortex-M4 kernel; when the CPU is operating at all allowed frequencies (< 168MHz), a program running in flash memory can achieve performance equivalent to zero latency. The STM32F4 series microcontroller integrates single-cycle DSP instructions and FPUs (floating point units), improves the computing power, and can perform complex computation and control.
The invention aims to solve the problem that the honeycomb labyrinth shortest path algorithm cannot be realized in a low-configuration single chip microcomputer, and adopts a high-efficiency algorithm under a low-cost hardware environment. The method can be used for various education training products, and can remarkably reduce the product cost.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, and the scope of protection is still within the scope of the invention.
Claims (7)
1. A honeycomb maze shortest path calculation method is characterized by comprising the following steps:
taking each node of the honeycomb maze as a unit structure body, wherein the attributes of the unit structure body comprise unit coordinates, pointers of adjacent nodes, unit node weight and the number of the adjacent nodes; numbering all nodes adjacent to each node, and storing pointers of the adjacent nodes into a unit structure body of the node;
reading data of nodes and edges, storing the data in an adjacent matrix form, and counting the number of the nodes and the number of the edges; the elements in the adjacent matrix are n and max, wherein n represents the weight value directly connected with edges, and max represents that no edges are directly connected;
in the operation process of the adjacent matrix, after each node traverses the adjacent node, the other connection points are all set as max;
and acquiring node information of the starting node, the end node and each intermediate node, and calculating the shortest distance from the starting point to the end point of each intermediate node by using a Dijkstra algorithm.
2. A honeycomb labyrinth shortest path calculation method according to claim 1, further comprising the steps of: nodes that the target path does not pass through are deleted from the network graph.
3. The method of claim 2, wherein the step of deleting nodes from the network graph that the target path does not pass through comprises: and starting from the starting point, deeply traversing the network graph, deleting nodes which are not traversed from the original network graph, and generating a new network graph to replace the original network graph.
4. The method of claim 1, further comprising, before storing the pointer of each neighboring node in the cell data set:
and when the unit coordinate position of a node is out of the area range of the network graph, deleting the node.
5. A honeycomb labyrinth training system, comprising:
the honeycomb unit is provided with a button for setting a starting point and an end point, and six electric bridges which are respectively communicated with six sides and used for sensing whether adjacent nodes are connected or not, wherein the six electric bridges are communicated with each other;
a master device communicatively coupled to each of the cells, the master device being capable of performing the cell maze shortest path computation method of any of claims 1-4.
6. A honeycomb labyrinth training system according to claim 5, characterized in that: and each honeycomb unit is provided with a signal lamp controlled by the main control equipment.
7. A honeycomb labyrinth training system according to claim 5, characterized in that: the master control equipment comprises an STM32 series control chip.
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