Engine pipeline path planning method

Abstract

The invention discloses an intelligent planning method for an engine pipeline path. The method comprises the following steps: S1, solving an eikonal equation of a three-dimensional distance field; s2, establishing a pipeline path planning method based on an A star algorithm; s3, algorithm optimization of actual pipeline arrangement is carried out; s4, performing instance testing, and optimizing an algorithm; and S5, running the program, and generating an engine pipeline path planning map. According to the method, the limited space of the engine is fully utilized to the maximum extent, meanwhile, an optimal path capable of avoiding obstacles is provided, and a scheme with few path bends is provided; the effects that the shortest path meets the process requirement, and the gap between the shortest path and peripheral parts meets the minimum gap requirement are achieved.

Description

Technical Field

[0001] The present invention relates to the field of engine pipelines, and in particular to a method for planning the path of an engine pipeline. Background Art

[0002] During the design and manufacturing process of an engine, the pipeline layout is a complex and crucial link. The drawing generation of traditional engine pipeline layout methods depends on the experience of designers. Therefore, there are many cases where the engine pipelines are bent at 90°, rather than being the shortest path; or the distance between the engine pipelines and various devices is too large. These settings reduce the utilization rate of the limited space inside the engine, and also lead to the situation that many emerging technologies can only arrange part devices in the gaps between the engine pipelines, which is not only inefficient but also limited by the layout space between the engine and the pipelines. In recent years, although some pipeline automatic layout technologies based on computer-aided design (CAD) have emerged, these technologies usually over-simplify the model structure, resulting in a large difference between the generated pipeline path and the actual requirements, and are mostly limited to orthogonal layouts, which do not meet the actual processing requirements.

[0003] Currently, the research on pipeline automatic layout technology mainly focuses on the field of ship pipeline design, and there is less research in the field of engine pipelines. However, existing methods all need to artificially simplify the obstacle structure into simple geometric bodies in advance, reducing the space where the pipeline can be arranged; at the same time, the generated pipeline directions are mostly orthogonal layouts, which cannot adapt to the compact and complex peripheral structure of the engine; the pipelines of the engine usually need to be connected and fixed to multiple points. When planning the path of the engine pipeline, the existing technology cannot provide path planning guidance under the condition of specifying the fixed points that the pipeline must pass through; the existing technology may generate multiple path schemes for the layout of the engine pipelines, and the existing technology cannot provide a direction for selecting or optimizing the pipeline scheme. Therefore, neither the existing manual planning nor the automatic planning can provide clear and effective technical guidance for the pipeline planning of the engine.

[0004] The information disclosed in this background art section is only intended to enhance the overall understanding of the present invention and should not be regarded as an admission or any form of suggestion that this information constitutes prior art already known to those of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to provide a method for planning the path of an engine pipeline, which can solve the problems existing in the background art.

[0006] To achieve the above object, the present invention provides an intelligent planning method for the engine pipeline path, which is characterized by including the steps of: S1: Solving the eikonal equation of the three-dimensional distance field; S2: Establishing a pipeline path planning method based on the A* algorithm; S3: Optimizing the algorithm for actual pipeline layout; S4: Conducting example tests and optimizing the algorithm; S5: Running the program and generating an engine pipeline path planning diagram.

[0007] In one or more embodiments, the step S1 includes: S11: Initializing the eikonal equation; S12: Approximately discretizing the eikonal equation; S13: Starting fast marching; S14: Generating a grid containing distance field information.

[0008] In one or more embodiments, the step S11 includes: S111: Setting A as the grid set of the points to be solved, and setting the distance of all grids to 0; S112: Setting NarrowBand as the grid set of all grids adjacent to the grids in A but not in A, and setting the distance of the grids in NarrowBand to the grid length h; S113: Setting the distance of the remaining grids not in the A and / or NarrowBand sets to ∞.

[0009] In one or more embodiments, the step S13 includes: S131: Starting a loop: Querying the grid with the minimum distance value in NarrowBand (); S132: Removing the grid () from the set NarrowBand and adding it to the set A; S133: Finding the grids that are not in the set A among the adjacent grids in the up, down, left, right, front, and back directions of the grid (), and if the grid is not in NarrowBand, adding the grid (i min ,j min ,k min ) to NarrowBand; S134: Recalculating the distance value in the grid according to the approximately discretized formula; S135: Returning to step S131 to start the next loop until the number of elements in the NarrowBand set is 0.

[0010] In one or more embodiments, the step S2 includes: S21: Initializing the grid, assigning the g value and h value in the grid to 0, setting the parent node to be empty, and initializing the CloseList list and OpenList list; S22: Setting the h value to the Manhattan distance from the starting point to the ending point, and adding the starting point to CloseList;

[0011] S23: Traverse the grids around the starting point, and set the g value of the surrounding grids of the starting point to the sum of the g value at the starting point and the g value of the grid movement method; add the surrounding grids of the starting point to the OpenList, and set the parent node of the surrounding grids of the starting point to the starting point; S24: Find the grid A with the minimum total cost in the current list of the OpenList, add the grid A to the CloseList, traverse the surrounding grids of the grid A, and update the g value and the parent node; S25: Repeat step S24 until the grid with the minimum total cost found in the openList is the end point or all grids have been traversed.

[0012] In one or more embodiments, the step S24 includes: S241: Traverse the surrounding grids of the grid A; add the surrounding grids that are different from the grids in the OpenList to the OpenList; S242: If the traversed grid belongs to the OpenList, denote this grid as grid B, and recalculate the current g value of the grid B to be the sum of the g value of the grid A and the g value of the grid movement method, and compare the current g value and the original g value of the grid B; S243: If the current g value of the grid B is less than the original g value, update the current g value and write it into the grid B, and update the parent node.

[0013] In one or more embodiments, the step S3 includes: S31: Set the pipeline fixing points; S32: Optimize using the pipeline layout algorithm of line fitting.

[0014] In one or more embodiments, the step S1 includes: S41: After selecting the boundary model, use the relevant API of NX secondary development to convert the model into a point set; S42: After selecting the starting point, end point, fixing point, inner and outer diameters of the pipeline, and the minimum obstacle clearance according to the interface, call the algorithm to generate a path; S43: Use the relevant API of secondary development to round the corners of the path to generate a three-dimensional model of the pipeline; S5: Run the program and generate an engine pipeline path planning diagram.

[0015] In a second aspect, the present invention provides an engine pipeline path planning system, including: a solving module for solving the eikonal equation of the three-dimensional distance field; a building module for building a pipeline path planning method based on the A* algorithm; an optimizing module for optimizing the algorithm for actual pipeline layout; a testing module for performing instance testing and optimizing the algorithm; a generating module for running the program and generating an engine pipeline path planning diagram.

[0016] Compared with the prior art, the multiple technical solutions and embodiments provided by the present invention at least include the following technical effects or advantages:

[0017] Through the A* algorithm and subsequent optimization, an optimal path that can avoid obstacles is provided, and it is admissible; by solving the eikonal equation of the three-dimensional distance field, the distance field is obtained, and path points are acquired, providing a means to make the most of the limited space of the engine to the greatest extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention, and can be considered as schematic results of various combinations of preferred embodiments, and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 is a schematic diagram of the overall process of a method for planning the path of an engine pipeline provided by the present invention;

[0020] Figure 2 is a schematic diagram of the decomposition process of step S1 of a method for planning the path of an engine pipeline provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Unless otherwise clearly stated, throughout the specification and claims, the term "comprising" or its variations such as "comprises" or "including" or "made of..." etc. will be understood to include the stated elements or components, without excluding other elements or other components.

[0022] The object of the present invention is to provide a method for planning the path of an engine pipeline, which realizes making the most of the limited space of the engine to the greatest extent, while providing an optimal path that can avoid obstacles, and has fewer path bends; the shortest path meets the process requirements, and the clearance with surrounding parts meets the minimum clearance requirement.

[0023] Example 1:

[0024] This embodiment provides a method for planning the path of an engine pipeline, including the steps of:

[0025] S1: Solve the eikonal equation of the three-dimensional distance field;

[0026] Specifically, in order to provide guidance for the layout of the engine pipeline and make full use of the limited layout space of the engine, this application provides a method for planning the path of the engine pipeline, which provides the optimal path for the engine pipeline planning through a computer algorithm. The distance field is a field used to represent the distance from each point in space to the nearest object. In practical applications, the eikonal equation is usually solved Calculate the distance field; the eikonal equation is a non-linear partial differential equation and can be regarded as an approximate wave equation. The physical meaning of T is the shortest time required for the curve to reach each point in the calculation domain at a speed of F(x), and the fast marching method is used to calculate and generate the distance field of the obstacles in the engine pipeline layout space.

[0027] As a preferred embodiment of this embodiment, the step S1 includes:

[0028] S11: Initialize the eikonal equation;

[0029] As a preferred embodiment of this embodiment, the step S11 includes:

[0030] S111: Set A as the grid set of the points to be solved, and the distances of all grids are set to 0;

[0031] S112: Set NarrowBand as the set of grids that are adjacent to the grids in A but not in A, and the distances of the grids in NarrowBand are set to the grid length h;

[0032] S113: Set the distances of the remaining grids not in the sets A and / or NarrowBand to ∞.

[0033] Specifically, in order to provide the initial value of the eikonal equation and determine the required range, by dividing the grid attributes and adding them to the set after judgment, the significance of set A is that set A limits the lower limit of the connection length by incorporating the connected points and the radius around the connected points; the significance of set NarrowBand is that set NarrowBand is used to save all optional subsequent connection points. By limiting the elements in the set, the value range of the subsequent connection points can be limited. When it is not required that the subsequent connection points appear in a certain range, it can be achieved by deleting the elements in set NarrowBand.

[0034] S12: Approximately discretize the eikonal equation;

[0035] Specifically, the eikonal equation can be approximately discretized as: In the formula Denoted as D i-1,j,k -D i,j,k , D i,j,k represents the distance from the grid (I, j, k) to the nearest object, and h is the grid length.

[0036] Approximate discretization is a method of converting continuous data into discrete data, mainly used to improve the spatio-temporal efficiency of algorithms. Its basic principle is to map finite individuals in an infinite space to a finite space, simplify the data processing process by reducing the data range without changing the relative magnitude of the data.

[0037] S13: Start fast marching;

[0038] S131: Start loop: Query the grid (i min , j min , k min ) with the minimum distance value in NarrowBand;

[0039] S132: Remove the grid (i mi n, j min , k min ) from the set NarrowBand and add it to the set A;

[0040] S133: Find the grids adjacent to the grid (i min , j min , k min ) in the up, down, left, right, front, and back directions that are not in the set A. If the grid is not in NarrowBand, add the grid (i min , j min , k min ) to NarrowBand;

[0041] S134: Recalculate the distance value in the grid according to the approximately discretized formula;

[0042] S135: Return to step S131 to start the next loop until the number of elements in the NarrowBand set is 0;

[0043] Specifically, the processing of the overall three-dimensional distance field is reduced to the processing of individual key grids and is achieved through multiple fast marching traversal loops. The fast marching method is an efficient numerical algorithm for solving the eikonal equation. The total arithmetic complexity of the fast marching method is low, and the computational efficiency is high; and it is a one-pass algorithm, reducing unnecessary calculations.

[0044] S14: Generate a grid containing distance field information.

[0045] S2: Establish a pipeline path planning method based on the A* algorithm;

[0046] Specifically, the A* algorithm is a deterministic path planning algorithm, which has the characteristics of simple mathematical model and high search efficiency. Its uniqueness lies in that when checking each possible node in the shortest path, it introduces global information, estimates the distance from the current node to the end point, and uses it as a measure to evaluate the possibility of the node being on the shortest route.

[0047] The A* algorithm maintains an open list (OpenList) during the search process, which contains candidate nodes to be considered. In each step, the algorithm selects the node with the smallest total cost in the open list for exploration. The total cost of this node usually consists of two parts: the known path length from the start point to this node (usually called the g value), and the estimated distance from this node to the target point (usually called the h value). By weighing the total cost, it evaluates whether the node is optimal, and then searches for the optimal solution.

[0048] Regardless of the situation, if a search algorithm can guarantee to find this shortest path, such a search algorithm is called admissibility; while the A* algorithm satisfies admissibility, the f values of all the sequences of the expanded nodes are increasing. Therefore, the path generated by the algorithm first must be the shortest, so it can also reduce the processing time and improve the processing speed.

[0049] As a preferred implementation manner of this embodiment, the step S2 includes:

[0050] S21: Initialize the grid, assign 0 to the g value and h value in the grid, set the parent node to be empty, and initialize the CloseList list and OpenList list;

[0051] S22: Set the h value to the Manhattan distance from the start point to the end point, and add the start point to the CloseList;

[0052] S23: Traverse the grids around the start point, and set the g value of the grids around the start point to the sum of the g value at the start point and the g value of the grid movement method; add the grids around the start point to the OpenList, and set the parent node of the grids around the start point to the start point;

[0053] Specifically, in order to search for subsequent nodes after finding each node, after each node is found, the parent node of the grids around the start point is set to the start point. That is, after each step of determining the node, the new node is regarded as the start point for the next step of subsequent search.

[0054] S24: Search for the grid A with the smallest total cost in the current list of the OpenList, add the grid A to the CloseList, traverse the grids around the grid A, and update the g value and the parent node.

[0055] Specifically, after multiple update traversals, the position information of all nodes is retained in the list, and the nodes are connected in series to find the optimal path.

[0056] As a preferred implementation manner of this embodiment, the step S24 includes:

[0057] S241: Traverse the surrounding grids of the grid A; add the surrounding grids that are different from the grids in the OpenList to the OpenList.

[0058] S242: If the traversed grid belongs to the OpenList, denote this grid as grid B, and recalculate the current g value of the grid B as the sum of the g value of the grid A and the g value of the grid movement mode, and compare the current g value and the original g value of the grid B.

[0059] S243: If the current g value of the grid B is less than the original g value, update and write the current g value into the grid B, and update the parent node.

[0060] Specifically, in this implementation manner, by setting the selection of points with the shortest distance and realizing path planning through connecting multiple points, the system selects path points within the range of the surrounding grids, takes the selected path points as new centers, determines and resets the surrounding grids; in the process of planning the shortest path for this application, points are selected at a certain interval, the shortest path is selected in each section of the surrounding grids, and connections are established section by section, and finally the optimal path is obtained; by comparing the current g value and the original g value of the path, the optimal path points are determined.

[0061] S25: Repeat step S24 until the grid with the smallest total cost found in the openList is the end point or all grids have been traversed.

[0062] Specifically, by verifying the total cost, each optimal path point is found; in this implementation manner, by pre-calculating the distance field of the grid to the obstacle point set, the A* algorithm only needs to perform path search on the grid points whose distances meet the minimum gap requirement, so as to avoid obstacles and meet the gap requirement at the same time.

[0063] S3: Optimize the algorithm for actual pipeline layout;

[0064] Specifically, after obtaining multiple path points, a way to fit a straight line is needed to minimize the bending of the pipeline as much as possible. However, by directly connecting the lines, although it seems that the pipeline route can be easily obtained, in fact, it increases the bending of the pipeline. The engine pipeline cannot provide too many bends during manufacturing, which will increase the manufacturing cost. Therefore, how to connect the path points becomes a major technical problem.

[0065] As a preferred implementation manner of this embodiment, the step S3 includes:

[0066] S31: Set pipeline fixed points;

[0067] S32: Optimize using a pipeline layout algorithm with linear fitting.

[0068] Specifically, this embodiment provides an optimal and popularizable linear fitting strategy. By sequentially judging the positional relationship of path points, the path of the fitting curve is determined, and the number of bends of the fitting curve is reduced. For example, assuming a given set of points with order and path tendency, the path points: point 1, point 2, and point 3 are arranged in sequence. Connect point 1 and point 3 to generate line segment L1. If the distance between point 2 and L1 is within the fitting allowable range, it is considered that points 1, 2, and 3 are on the same straight line, and then L1 is determined as the fitted straight line; if point 2 is not within the fitting allowable range of L1, then the end point of L1 is changed to point 2, and a new line segment L2 is created. The starting point of L2 is point 2, and the end point is point 3. By traversing every 3 adjacent nodes, the complete connection is obtained, improving the manufacturability of the generated pipeline.

[0069] In terms of reducing pipeline bends, this embodiment determines whether the grid n with the minimum total cost in the current list found in the OpenList next is collinear with its parent nodes n - 1 and n - 2. If it is collinear, then the h value of grid n is multiplied by a reward coefficient (between 0 and 1) based on the h value of the traditional A* algorithm, making the priority of adding this grid to the CloseList higher; at the same time, during the linear fitting process, it is also necessary to judge whether the nodes passed by the generated line segment meet the minimum clearance.

[0070] Among them, the pipeline fixed points in step S31 refer to the points that the engine pipeline is forced to pass through. The reason is that the engine pipeline often needs to be connected to multiple devices, or the engine pipeline is too long and needs to be fixed at a preset position. When the pipeline fixed points are set, this embodiment first determines the shortest path of the pipeline passing through all the connections of the fixed points according to the positional relationship between the fixed points and the start and end points, and obtains the passing order of the path points; then, according to the passing order of the path points, they are divided into different sections in sequence, and the two end points of each section are used as the start and end points to plan the shortest path, and finally the paths of multiple sections are integrated to obtain the shortest path of the overall pipeline with comprehensive fixed points.

[0071] S4: Conduct instance testing to optimize the algorithm.

[0072] Specifically, this embodiment further optimizes the algorithm, reduces the loss through instance testing and training, and finally enables the algorithm to obtain the best path more accurately.

[0073] As a preferred embodiment of this example, step S4 includes:

[0074] S41: After selecting the boundary model, use the relevant API of NX secondary development to convert the model into a point set;

[0075] S42: After selecting the starting point, ending point, fixed point, inner and outer diameters of the pipeline, and the minimum obstacle clearance according to the interface, call the algorithm to generate a path;

[0076] S43: And use the relevant APIs for secondary development to round the corners of the path and generate a 3D model of the pipeline.

[0077] Specifically, NX refers to Siemens' Unigraphics NX software, which is widely used in various design fields. NX is powerful and provides complete tools and documents for secondary development. Developers can choose programming languages such as KF, C, C++, C#, Java, etc. for secondary development according to their own capabilities. In this article, the C# programming language and the UI development tool BlockUI Styler are used for code development and UI design.

[0078] During actual debugging, the hardware configuration is CPU: AMD Ryzen 7 5800X 3.8GHz, memory: 16GB, GPU AMD Radeon RX 6600XT. After selecting the boundary model, use the relevant APIs for NX secondary development to convert the model into a point set. After selecting the starting point, ending point, fixed point, inner and outer diameters of the pipeline, and the minimum obstacle clearance according to the interface, call the algorithm to generate a path, and use the relevant APIs for NX secondary development to round the corners of the path and generate a 3D model of the pipeline. At the same time, the generated pipeline model also provides corresponding nodes for designers to adjust.

[0079] S5: The program runs and generates a path planning diagram for the engine pipeline.

[0080] According to actual tests, the final generated model space size is: 200mm×600mm×250mm; pipe diameter: 12mm; minimum clearance requirement: 10mm; grid cell length: 2mm.

[0081] The pipeline generated in this embodiment makes the most of the limited space of the engine while providing the optimal path that can avoid obstacles, and there are fewer bends in the path; the shortest path meets the process requirements, and the clearance with surrounding parts meets the minimum clearance requirements.

[0082] Embodiment 2:

[0083] This embodiment provides an engine pipeline path planning system. Based on the same concept, this embodiment is used to implement an engine pipeline path planning method as described in Embodiment 1, including:

[0084] A solving module, used to solve the eikonal equation of the three-dimensional distance field;

[0085] A building module, configured to build a pipeline path planning method based on the A* algorithm;

[0086] An optimization module, configured to optimize the algorithm for actual pipeline layout;

[0087] A testing module, configured to conduct example tests and optimize the algorithm;

[0088] A generation module, configured to run the program and generate an engine pipeline path planning diagram.

[0089] The foregoing description of specific exemplary embodiments of the present invention is for purposes of illustration and exemplification. These descriptions are not intended to limit the present invention to the precise forms disclosed, and it is apparent that many changes and variations are possible in light of the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical applications, so that those skilled in the art can implement and utilize various different exemplary embodiments of the present invention, as well as various different selections and changes. The scope of the present invention is intended to be defined by the claims and their equivalents.

Claims

1. An intelligent planning method for engine pipeline path, characterized in that: Includes steps: S1: Solve the eikonal equation for the three-dimensional distance field; S2: Establish a pipeline path planning method based on the A-star algorithm; S3: Perform algorithm optimization for actual pipeline layout; S4: Conduct instance tests and optimize algorithms; S5: The program runs and generates an engine pipeline path planning diagram.

2. The method for intelligent planning of engine pipeline paths according to claim 1, characterized in that: The step S1 comprises: S11: Initialize the eikonal equation; S12: approximately discretizing the eikonal equation; S13: Start to move fast; S14: Generate a mesh containing distance field information.

3. The method for intelligent planning of engine pipeline paths according to claim 2, characterized in that: The step S11 comprises: S111: Set A as a grid set of the required solution point set, and the distances of all grids are set to 0; S112: NarrowBand is set to be a set of all grids that are adjacent to grids in A but not in A, and the distances between grids in NarrowBand are all set to the grid length h; S113: Set the distances of the remaining grids that are not in the A and / or NarrowBand set to ∞.

4. The method for intelligent planning of engine pipeline paths according to claim 2, characterized in that: The step S13 comprises: S131: Start loop: query the grid with the smallest distance value in NarrowBand (i min ,j min ,k min ); S132: The grid (i min ,j min ,k min ) is removed from the set NarrowBand and added to the set A; S133: Find the grid (i min ,j min ,k min ) A grid that is not in set A among the adjacent grids in the up, down, left, right, front and back directions. If the grid is not in NarrowBand, the grid (i min ,j min ,k min ) is added to NarrowBand; S134: recalculating the distance value in the grid according to the approximate discretization formula; S135: Return to step S131 to start the next loop until the number of elements in the NarrowBand set is 0.

5. The engine pipeline path intelligent planning method according to claim 1, characterized in that: The step S2 comprises: S21: Initialize the grid, set the g value and h value in the grid to 0, set the parent node to empty, and initialize the CloseList list and OpenList list; S22: setting the h value to the Manhattan distance from the starting point to the end point, and adding the starting point to the CloseList; S23: traverse the surrounding grids of the starting point, and set the g value of the surrounding grids of the starting point to be the sum of the g value at the starting point and the g value of the grid movement mode; add the surrounding grids of the starting point to OpenList, and set the parent node of the surrounding grids of the starting point to be the starting point; S24: Find the grid A with the smallest total cost in the current list of OpenList, add the grid A to CloseList, traverse the surrounding grids of the grid A, and update the g value and the parent node; S25: Repeat step S24 until the grid with the smallest total cost found in the openList is the end point or the traversal of all grids is completed.

6. The method for intelligent planning of engine pipeline paths according to claim 5, characterized in that: The step S24 comprises: S241: traverse the surrounding grids of the grid A; add the surrounding grids that are different from the grids in the OpenList to the OpenList; S242: If the traversed grid belongs to the OpenList, record this grid as grid B, recalculate the current g value of the grid B as the sum of the g value of the grid A and the g value of the grid movement mode, and compare the current g value of the grid B with the original g value; S243: If the current g value of the grid B is less than the original g value, the current g value is updated and written into the grid B, and the parent node is updated.

7. The method for intelligent planning of engine pipeline paths according to claim 1, characterized in that: The step S3 comprises: S31: Set the pipeline fixing point; S32: Pipeline layout algorithm optimization using straight line fitting.

8. The method for intelligent planning of engine pipeline paths according to claim 1, characterized in that: The step S1 comprises: S41: After selecting the boundary model, use the NX secondary development related API to convert the model into a point set; S42: after selecting the starting point, end point, fixed point, inner and outer diameters of the pipeline and the minimum obstacle gap on the interface, calling the algorithm to generate a path; S43: Use the secondary development related API to round the path and generate a three-dimensional model of the pipeline; S5: The program runs and generates an engine pipeline path planning diagram.

9. An engine pipeline path planning system, based on the same concept as the engine pipeline path planning method according to any one of claims 1 to 8, comprising: A solver module for solving the eikonal equation of the three-dimensional distance field; Establish a module for establishing a pipeline path planning method based on the A-star algorithm; Optimization module, used to optimize the algorithm of actual pipeline layout; Testing module, used for instance testing and algorithm optimization; The generation module is used to run the program and generate the engine pipeline path planning diagram.

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